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Few-shot learning, Grafting, Self-supervision, Distillation,
Deep learning, Object recognition
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2105
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2104
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WACV21(1338-1346)
IEEE DOI
2106
Deep learning, Convolutional NNs, Knowledge distillation,
Unsupervised domain adaptation, CNN acceleration and compression.
Adaptation models, Computational modeling,
Benchmark testing, Real-time systems
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Remigereau, F.[Félix],
Mekhazni, D.[Djebril],
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Knowledge Distillation for Multi-Target Domain Adaptation in
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ICIP22(3853-3557)
IEEE DOI
2211
Training, Adaptation models, Scalability, Streaming media,
Video surveillance, IEEE Standards, Video Surveillance,
Knowledge Distillation
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Zhang, H.R.[Hao-Ran],
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PR(111), 2021, pp. 107659.
Elsevier DOI
2012
Knowledge distillation, Data augmentation,
Generative adversarial nets, Divergent examples, Image classification
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Gou, J.P.[Jian-Ping],
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Maybank, S.J.[Stephen J.],
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IJCV(129), No. 6, June 2021, pp. 1789-1819.
Springer DOI
2106
Survey, Knowledge Distillation.
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Liu, Y.F.[Yu-Fan],
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Maybank, S.J.[Stephen J.],
Cross-Architecture Knowledge Distillation,
IJCV(132), No. 8, August 2024, pp. 2798-2824.
Springer DOI
2408
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Tian, X.D.[Xu-Dong],
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Wang, C.[Cong],
Zhang, W.S.[Wen-Sheng],
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Ma, L.Z.[Li-Zhuang],
Wu, Z.Z.[Zong-Ze],
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Variational Distillation for Multi-View Learning,
PAMI(46), No. 7, July 2024, pp. 4551-4566.
IEEE DOI
2406
Mutual information, Task analysis, Representation learning,
Predictive models, Optimization, Visualization, Pattern analysis,
knowledge distillation
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Deng, Y.J.[Yong-Jian],
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Learning From Images:
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IP(30), 2021, pp. 4919-4931.
IEEE DOI
2106
Task analysis, Feature extraction, Cameras, Data models,
Streaming media, Trajectory, Power demand, Event-based vision,
optical flow prediction
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Liu, Y.[Yang],
Wang, K.[Keze],
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Lin, L.[Liang],
Semantics-Aware Adaptive Knowledge Distillation for Sensor-to-Vision
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IP(30), 2021, pp. 5573-5588.
IEEE DOI
2106
Videos, Knowledge engineering, Wearable sensors, Adaptation models,
Sensors, Semantics, Image synthesis, Action recognition,
transfer learning
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Feng, Z.X.[Zhan-Xiang],
Lai, J.H.[Jian-Huang],
Xie, X.H.[Xiao-Hua],
Resolution-Aware Knowledge Distillation for Efficient Inference,
IP(30), 2021, pp. 6985-6996.
IEEE DOI
2108
Knowledge engineering, Feature extraction, Image resolution,
Computational modeling, Computational complexity, Image coding,
adversarial learning
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Liu, Y.Y.[Yu-Yang],
Cong, Y.[Yang],
Sun, G.[Gan],
Zhang, T.[Tao],
Dong, J.H.[Jia-Hua],
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L3DOC: Lifelong 3D Object Classification,
IP(30), 2021, pp. 7486-7498.
IEEE DOI
2109
Task analysis, Solid modeling,
Data models, Knowledge engineering, Shape, Robots,
task-relevant knowledge distillation
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Bhardwaj, A.[Ayush],
Pimpale, S.[Sakshee],
Kumar, S.[Saurabh],
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Empowering Knowledge Distillation via Open Set Recognition for Robust
3D Point Cloud Classification,
PRL(151), 2021, pp. 172-179.
Elsevier DOI
2110
Knowledge Distillation, Open Set Recognition,
3D Object Recognition, Point Cloud Classification
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Shao, B.[Baitan],
Chen, Y.[Ying],
Multi-granularity for knowledge distillation,
IVC(115), 2021, pp. 104286.
Elsevier DOI
2110
Knowledge distillation, Model compression,
Multi-granularity distillation mechanism, Stable excitation scheme
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Zhang, L.[Libo],
Du, D.W.[Da-Wei],
Li, C.C.[Cong-Cong],
Wu, Y.J.[Yan-Jun],
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Iterative Knowledge Distillation for Automatic Check-Out,
MultMed(23), 2021, pp. 4158-4170.
IEEE DOI
2112
Testing, Training, Adaptation models, Reliability,
Feature extraction, Training data, Task analysis,
iterative knowledge distillation
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Qin, D.[Dian],
Bu, J.J.[Jia-Jun],
Liu, Z.[Zhe],
Shen, X.[Xin],
Zhou, S.[Sheng],
Gu, J.J.[Jing-Jun],
Wang, Z.H.[Zhi-Hua],
Wu, L.[Lei],
Dai, H.F.[Hui-Fen],
Efficient Medical Image Segmentation Based on Knowledge Distillation,
MedImg(40), No. 12, December 2021, pp. 3820-3831.
IEEE DOI
2112
Image segmentation, Biomedical imaging, Semantics,
Knowledge engineering, Feature extraction, Tumors,
transfer learning
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Tian, L.[Ling],
Wang, Z.C.[Zhi-Chao],
He, B.[Bokun],
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Knowledge Distillation of Grassmann Manifold Network for Remote
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RS(13), No. 22, 2021, pp. xx-yy.
DOI Link
2112
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Yue, J.[Jun],
Fang, L.Y.[Le-Yuan],
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Self-Supervised Learning With Adaptive Distillation for Hyperspectral
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GeoRS(60), 2022, pp. 1-13.
IEEE DOI
2112
Feature extraction, Training, Adaptive systems, Mirrors,
Knowledge engineering, Hyperspectral imaging, Spectral analysis,
spatial-spectral feature extraction
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Ding, K.[Kexin],
Lu, T.[Ting],
Fu, W.[Wei],
Fang, L.Y.[Le-Yuan],
Cross-Scene Hyperspectral Image Classification With Consistency-Aware
Customized Learning,
CirSysVideo(35), No. 1, January 2025, pp. 418-430.
IEEE DOI
2502
Feature extraction, Training, Prototypes, Representation learning,
Data mining, Laser radar, Hyperspectral imaging,
contrastive learning
BibRef
Chen, H.Y.[Hong-Yuan],
Pei, Y.T.[Yan-Ting],
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Super-resolution guided knowledge distillation for low-resolution
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PRL(155), 2022, pp. 62-68.
Elsevier DOI
2203
Low-resolution image classification, Super-resolution, Knowledge distillation
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Wang, S.L.[Shu-Ling],
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Self-Paced Knowledge Distillation for Real-Time Image Guided Depth
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SPLetters(29), No. 2022, pp. 867-871.
IEEE DOI
2204
Knowledge engineering, Predictive models, Training, Task analysis,
Real-time systems, Color, Loss measurement, self-paced learning
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Ye, J.W.[Jing-Wen],
Song, M.L.[Ming-Li],
Spot-Adaptive Knowledge Distillation,
IP(31), 2022, pp. 3359-3370.
IEEE DOI
2205
Knowledge engineering, Training, Routing, Data models,
Adaptation models, Deep learning, Training data, spot-adaptive distillation
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Xie, L.X.[Ling-Xi],
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Progressive privileged knowledge distillation for online action
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PR(129), 2022, pp. 108741.
Elsevier DOI
2206
Online action detection, Knowledge distillation,
Privileged information, Curriculum learning
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Zhao, H.R.[Hao-Ran],
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Gao, F.[Feng],
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Pair-Wise Similarity Knowledge Distillation for RSI Scene
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RS(14), No. 10, 2022, pp. xx-yy.
DOI Link
2206
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Li, K.[Kunchi],
Wan, J.[Jun],
Yu, S.[Shan],
CKDF: Cascaded Knowledge Distillation Framework for Robust
Incremental Learning,
IP(31), 2022, pp. 3825-3837.
IEEE DOI
2206
Task analysis, Computational modeling, Adaptation models,
Data models, Training, Knowledge engineering, Feature extraction,
incremental learning
BibRef
Zhou, H.[Haonan],
Du, X.P.[Xiao-Ping],
Li, S.[Sen],
Self-Supervision and Self-Distillation with Multilayer Feature
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RS(14), No. 13, 2022, pp. xx-yy.
DOI Link
2208
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Chi, Q.[Qiang],
Lv, G.H.[Guo-Hua],
Zhao, G.X.[Gui-Xin],
Dong, X.J.[Xiang-Jun],
A Novel Knowledge Distillation Method for Self-Supervised
Hyperspectral Image Classification,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
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Hao, Z.W.[Zhi-Wei],
Luo, Y.[Yong],
Wang, Z.[Zhi],
Hu, H.[Han],
An, J.P.[Jian-Ping],
CDFKD-MFS: Collaborative Data-Free Knowledge Distillation via
Multi-Level Feature Sharing,
MultMed(24), 2022, pp. 4262-4274.
IEEE DOI
2210
Generators, Knowledge engineering, Computational modeling,
Aggregates, Predictive models, Collaboration, Model Compression,
Attention
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Zhao, Y.[Yibo],
Liu, J.J.[Jian-Jun],
Yang, J.L.[Jin-Long],
Wu, Z.B.[Ze-Bin],
Remote Sensing Image Scene Classification via Self-Supervised
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RS(14), No. 19, 2022, pp. xx-yy.
DOI Link
2210
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Hou, J.W.[Jing-Wen],
Ding, H.H.[Heng-Hui],
Lin, W.S.[Wei-Si],
Liu, W.[Weide],
Fang, Y.M.[Yu-Ming],
Distilling Knowledge From Object Classification to Aesthetics
Assessment,
CirSysVideo(32), No. 11, November 2022, pp. 7386-7402.
IEEE DOI
2211
Semantics, Computational modeling, Predictive models,
Feature extraction, Training, Pattern matching, Visualization,
image aesthetics assessment
BibRef
Xing, S.Y.[Shi-Yi],
Xing, J.S.[Jin-Sheng],
Ju, J.G.[Jian-Guo],
Hou, Q.S.[Qing-Shan],
Ding, X.R.[Xiu-Rui],
Collaborative Consistent Knowledge Distillation Framework for Remote
Sensing Image Scene Classification Network,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link
2211
BibRef
Xu, H.T.[Hong-Teng],
Liu, J.C.[Jia-Chang],
Luo, D.[Dixin],
Carin, L.[Lawrence],
Representing Graphs via Gromov-Wasserstein Factorization,
PAMI(45), No. 1, January 2023, pp. 999-1016.
IEEE DOI
2212
Kernel, Computational modeling, Task analysis, Message passing,
Graph neural networks, Data models, Graph representation,
permutation-invariance
BibRef
Chen, L.Q.[Li-Qun],
Wang, D.[Dong],
Gan, Z.[Zhe],
Liu, J.J.[Jing-Jing],
Henao, R.[Ricardo],
Carin, L.[Lawrence],
Wasserstein Contrastive Representation Distillation,
CVPR21(16291-16300)
IEEE DOI
2111
Knowledge engineering, Measurement,
Computational modeling, Collaborative work, Robustness
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Ye, H.J.[Han-Jia],
Lu, S.[Su],
Zhan, D.C.[De-Chuan],
Generalized Knowledge Distillation via Relationship Matching,
PAMI(45), No. 2, February 2023, pp. 1817-1834.
IEEE DOI
2301
Task analysis, Training, Neural networks, Knowledge engineering,
Deep learning, Standards, Training data, Cross-Task,
representation learning
BibRef
Xu, G.D.[Guo-Dong],
Liu, Z.W.[Zi-Wei],
Loy, C.C.[Chen Change],
Computation-Efficient Knowledge Distillation via Uncertainty-Aware
Mixup,
PR(138), 2023, pp. 109338.
Elsevier DOI
2303
Knowledge distillation, Training cost
BibRef
Yu, X.[Xinyi],
Yan, L.[Ling],
Yang, Y.[Yang],
Zhou, L.[Libo],
Ou, L.L.[Lin-Lin],
Conditional generative data-free knowledge distillation,
IVC(131), 2023, pp. 104627.
Elsevier DOI
2303
Data-free knowledge distillation, Generative adversarial networks,
Model compression, Convolutional neural networks
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Su, T.T.[Tong-Tong],
Liang, Q.Y.[Qi-Yu],
Zhang, J.S.[Jin-Song],
Yu, Z.Y.[Zhao-Yang],
Xu, Z.Y.[Zi-Yue],
Wang, G.[Gang],
Liu, X.G.[Xiao-Guang],
Deep Cross-Layer Collaborative Learning Network for Online Knowledge
Distillation,
CirSysVideo(33), No. 5, May 2023, pp. 2075-2087.
IEEE DOI
2305
Knowledge engineering, Cross layer design, Training,
Feature extraction, Visualization, Collaboration,
model compression
BibRef
Tang, Y.[Yuan],
Chen, Y.[Ying],
Xie, L.[Linbo],
Self-knowledge distillation based on knowledge transfer from soft to
hard examples,
IVC(135), 2023, pp. 104700.
Elsevier DOI
2306
Model compression, Self-knowledge distillation, Hard examples,
Class probability consistency, Memory bank
BibRef
Lee, H.[Hyoje],
Park, Y.[Yeachan],
Seo, H.[Hyun],
Kang, M.[Myungjoo],
Self-knowledge distillation via dropout,
CVIU(233), 2023, pp. 103720.
Elsevier DOI
2307
Deep learning, Knowledge distillation,
Self-knowledge distillation, Regularization, Dropout
BibRef
Yang, C.G.[Chuan-Guang],
An, Z.L.[Zhu-Lin],
Zhou, H.[Helong],
Zhuang, F.Z.[Fu-Zhen],
Xu, Y.J.[Yong-Jun],
Zhang, Q.[Qian],
Online Knowledge Distillation via Mutual Contrastive Learning for
Visual Recognition,
PAMI(45), No. 8, August 2023, pp. 10212-10227.
IEEE DOI
2307
Visualization, Knowledge engineering, Training, Task analysis,
Federated learning, Dogs, Computational modeling, visual recognition
BibRef
Fang, H.X.[Hang-Xiang],
Long, Y.[Yongwen],
Hu, X.[Xinyi],
Ou, Y.T.[Yang-Tao],
Huang, Y.[Yuanjia],
Hu, H.J.[Hao-Ji],
Dual cross knowledge distillation for image super-resolution,
JVCIR(95), 2023, pp. 103858.
Elsevier DOI
2309
Super resolution, Knowledge distillation, Convolutional neural networks
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Yang, Z.[Zhen],
Cao, Y.[Ying],
Zhou, X.[Xin],
Liu, J.[Junya],
Zhang, T.[Tao],
Ji, J.S.[Jin-Sheng],
Random Shuffling Data for Hyperspectral Image Classification with
Siamese and Knowledge Distillation Network,
RS(15), No. 16, 2023, pp. 4078.
DOI Link
2309
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Cavazza, J.[Jacopo],
Murino, V.[Vittorio],
Bue, A.D.[Alessio Del],
No Adversaries to Zero-Shot Learning:
Distilling an Ensemble of Gaussian Feature Generators,
PAMI(45), No. 10, October 2023, pp. 12167-12178.
IEEE DOI
2310
BibRef
Shao, R.R.[Ren-Rong],
Zhang, W.[Wei],
Wang, J.[Jun],
Conditional pseudo-supervised contrast for data-Free knowledge
distillation,
PR(143), 2023, pp. 109781.
Elsevier DOI
2310
Model compression, Knowledge distillation,
Representation learning, Contrastive learning, Privacy protection
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Shao, R.R.[Ren-Rong],
Zhang, W.[Wei],
Yin, J.H.[Jian-Hua],
Wang, J.[Jun],
Data-free Knowledge Distillation for Fine-grained Visual
Categorization,
ICCV23(1515-1525)
IEEE DOI Code:
WWW Link.
2401
BibRef
López-Cifuentes, A.[Alejandro],
Escudero-Viñolo, M.[Marcos],
Bescós, J.[Jesús],
Miguel, J.C.S.[Juan C. San],
Attention-Based Knowledge Distillation in Scene Recognition:
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CirSysVideo(33), No. 9, September 2023, pp. 4769-4783.
IEEE DOI Code:
WWW Link.
2310
BibRef
Ma, W.T.[Wen-Tao],
Chen, Q.C.[Qing-Chao],
Zhou, T.Q.[Tong-Qing],
Zhao, S.[Shan],
Cai, Z.P.[Zhi-Ping],
Using Multimodal Contrastive Knowledge Distillation for Video-Text
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CirSysVideo(33), No. 10, October 2023, pp. 5486-5497.
IEEE DOI
2310
BibRef
Zhang, L.F.[Lin-Feng],
Ma, K.[Kaisheng],
Structured Knowledge Distillation for Accurate and Efficient Object
Detection,
PAMI(45), No. 12, December 2023, pp. 15706-15724.
IEEE DOI
2311
BibRef
Zhang, L.F.[Lin-Feng],
Dong, R.[Runpei],
Tai, H.S.[Hung-Shuo],
Ma, K.[Kaisheng],
PointDistiller: Structured Knowledge Distillation Towards Efficient
and Compact 3D Detection,
CVPR23(21791-21801)
IEEE DOI
2309
BibRef
Yue, H.[Han],
Li, J.D.[Jun-Dong],
Liu, H.F.[Hong-Fu],
Second-Order Unsupervised Feature Selection via Knowledge Contrastive
Distillation,
PAMI(45), No. 12, December 2023, pp. 15577-15587.
IEEE DOI
2311
BibRef
Yu, X.T.[Xiao-Tong],
Sun, S.[Shiding],
Tian, Y.J.[Ying-Jie],
Self-distillation and self-supervision for partial label learning,
PR(146), 2024, pp. 110016.
Elsevier DOI
2311
Knowledge distillation, Self-supervised learning,
Partial label learning, Machine learning
BibRef
Yang, S.Z.[Shun-Zhi],
Xu, L.C.[Liu-Chi],
Zhou, M.C.[Meng-Chu],
Yang, X.[Xiong],
Yang, J.F.[Jin-Feng],
Huang, Z.H.[Zhen-Hua],
Skill-Transferring Knowledge Distillation Method,
CirSysVideo(33), No. 11, November 2023, pp. 6487-6502.
IEEE DOI
2311
BibRef
Zhao, Q.[Qi],
Lyu, S.C.[Shu-Chang],
Chen, L.[Lijiang],
Liu, B.[Binghao],
Xu, T.B.[Ting-Bing],
Cheng, G.L.[Guang-Liang],
Feng, W.[Wenquan],
Learn by Oneself: Exploiting Weight-Sharing Potential in Knowledge
Distillation Guided Ensemble Network,
CirSysVideo(33), No. 11, November 2023, pp. 6661-6678.
IEEE DOI Code:
WWW Link.
2311
BibRef
Li, X.F.[Xiu-Fang],
Sun, Q.G.[Qi-Gong],
Jiao, L.C.[Li-Cheng],
Liu, F.[Fang],
Liu, X.[Xu],
Li, L.L.[Ling-Ling],
Chen, P.[Puhua],
Zuo, Y.[Yi],
D^3K: Dynastic Data-Free Knowledge Distillation,
MultMed(25), 2023, pp. 8358-8371.
IEEE DOI
2312
BibRef
Wang, J.H.[Jun-Huang],
Zhang, W.W.[Wei-Wei],
Guo, Y.F.[Yu-Feng],
Liang, P.[Peng],
Ji, M.[Ming],
Zhen, C.H.[Cheng-Hui],
Wang, H.[Hanmeng],
Global key knowledge distillation framework,
CVIU(239), 2024, pp. 103902.
Elsevier DOI
2402
Deep learning, Knowledge distillation, Self-distillation,
Convolutional neural network
BibRef
Yu, H.[Hao],
Feng, X.[Xin],
Wang, Y.L.[Yun-Long],
Enhancing deep feature representation in self-knowledge distillation
via pyramid feature refinement,
PRL(178), 2024, pp. 35-42.
Elsevier DOI Code:
WWW Link.
2402
Self-knowledge distillation, Feature representation,
Pyramid structure, Deep neural networks
BibRef
Bao, Z.Q.[Zhi-Qiang],
Chen, Z.H.[Zi-Hao],
Wang, C.D.[Chang-Dong],
Zheng, W.S.[Wei-Shi],
Huang, Z.H.[Zhen-Hua],
Chen, Y.W.[Yun-Wen],
Post-Distillation via Neural Resuscitation,
MultMed(26), 2024, pp. 3046-3060.
IEEE DOI
2402
Neurons, Computational modeling, Standards, Optimization,
Knowledge engineering, Task analysis, Probabilistic logic, transfer learning
BibRef
Ma, X.[Xin],
Liu, C.[Chang],
Xie, C.Y.[Chun-Yu],
Ye, L.[Long],
Deng, Y.F.[Ya-Feng],
Ji, X.Y.[Xiang-Yang],
Disjoint Masking With Joint Distillation for Efficient Masked Image
Modeling,
MultMed(26), 2024, pp. 3077-3087.
IEEE DOI
2402
Training, Image reconstruction, Predictive models, Task analysis,
Visualization, Convergence, Computational modeling, and training efficiency
BibRef
Xu, Z.[Zhi],
Fu, Z.Y.[Zhen-Yong],
Using Mixture of Experts to accelerate dataset distillation,
JVCIR(100), 2024, pp. 104137.
Elsevier DOI
2405
Dataset distillation, Mixture of experts, Accelerate
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Mei, Z.[Zhen],
Ye, P.[Peng],
Li, B.[Baopu],
Chen, T.[Tao],
Fan, J.Y.[Jia-Yuan],
Ouyang, W.L.[Wan-Li],
DeNKD: Decoupled Non-Target Knowledge Distillation for Complementing
Transformer-Based Unsupervised Domain Adaptation,
CirSysVideo(34), No. 5, May 2024, pp. 3220-3231.
IEEE DOI
2405
Transformers, Task analysis, Semantics, Adaptation models,
Knowledge transfer, Visualization, Training, Transformer,
knowledge distillation
BibRef
Liang, G.Q.[Guo-Qiang],
Chen, Z.J.[Zhao-Jie],
Chen, Z.Q.[Zhao-Qiang],
Ji, S.Y.[Shi-Yu],
Zhang, Y.N.[Yan-Ning],
New Insights on Relieving Task-Recency Bias for Online Class
Incremental Learning,
CirSysVideo(34), No. 5, May 2024, pp. 3451-3464.
IEEE DOI Code:
WWW Link.
2405
Task analysis, Data models, Training, Streaming media,
Stability criteria, Predictive models, Circuit stability,
virtual knowledge distillation
BibRef
Xu, L.C.[Liu-Chi],
Ren, J.[Jin],
Huang, Z.H.[Zhen-Hua],
Zheng, W.S.[Wei-Shi],
Chen, Y.W.[Yun-Wen],
Improving Knowledge Distillation via Head and Tail Categories,
CirSysVideo(34), No. 5, May 2024, pp. 3465-3480.
IEEE DOI
2405
Tail, Head, Knowledge transfer, Task analysis, Knowledge engineering,
Image classification, Training, Knowledge distillation,
instance segmentation
BibRef
Jang, J.Y.[Jae-Yeon],
Synthetic unknown class learning for learning unknowns,
PR(153), 2024, pp. 110560.
Elsevier DOI
2405
Open set recognition, Overgeneralization,
Knowledge distillation, Generative adversarial learning, Unknown
BibRef
Zhu, S.L.[Song-Ling],
Shang, R.H.[Rong-Hua],
Yuan, B.[Bo],
Zhang, W.[Weitong],
Li, W.J.[Wen-Jie],
Li, Y.Y.[Yang-Yang],
Jiao, L.C.[Li-Cheng],
DynamicKD: An effective knowledge distillation via dynamic entropy
correction-based distillation for gap optimizing,
PR(153), 2024, pp. 110545.
Elsevier DOI
2405
Convolutional neural networks, Knowledge distillation,
CNN compression, CNN acceleration
BibRef
Guo, Z.[Zhen],
Zhang, P.Z.[Peng-Zhou],
Liang, P.[Peng],
SAKD: Sparse attention knowledge distillation,
IVC(146), 2024, pp. 105020.
Elsevier DOI
2405
Knowledge distillation, Attention mechanisms, Sparse attention mechanisms
BibRef
Li, C.[Cong],
Cheng, G.[Gong],
Han, J.W.[Jun-Wei],
Boosting Knowledge Distillation via Intra-Class Logit Distribution
Smoothing,
CirSysVideo(34), No. 6, June 2024, pp. 4190-4201.
IEEE DOI Code:
WWW Link.
2406
Training, Smoothing methods, Analytical models, Standards,
Data models, Correlation, Boosting, Knowledge distillation,
image classification
BibRef
Zhang, S.[Sha],
Deng, J.J.[Jia-Jun],
Bai, L.[Lei],
Li, H.Q.[Hou-Qiang],
Ouyang, W.L.[Wan-Li],
Zhang, Y.Y.[Yan-Yong],
HVDistill: Transferring Knowledge from Images to Point Clouds via
Unsupervised Hybrid-View Distillation,
IJCV(132), No. 7, July 2024, pp. Pages2585-2599.
Springer DOI
2406
BibRef
Wang, Y.[Yang],
Qian, B.[Biao],
Liu, H.P.[Hai-Peng],
Rui, Y.[Yong],
Wang, M.[Meng],
Unpacking the Gap Box Against Data-Free Knowledge Distillation,
PAMI(46), No. 9, September 2024, pp. 6280-6291.
IEEE DOI
2408
Training, Art, Data models, Analytical models, Knowledge engineering,
Generators, Data-free knowledge distillation, derived gap,
inherent gap
BibRef
Li, S.Y.[Shu-Yi],
Hu, H.C.[Hong-Chao],
Huo, S.[Shumin],
Liang, H.[Hao],
Clean, performance-robust, and performance-sensitive historical
information based adversarial self-distillation,
IET-CV(18), No. 5, 2024, pp. 591-612.
DOI Link
2408
architecture, convolutional neural nets,
image classification, image sampling, image sequences
BibRef
Zhang, W.W.[Wei-Wei],
Guo, Y.F.[Yu-Feng],
Wang, J.[Junhuang],
Zhu, J.Q.[Jian-Qing],
Zeng, H.Q.[Huan-Qiang],
Collaborative Knowledge Distillation,
CirSysVideo(34), No. 8, August 2024, pp. 7601-7613.
IEEE DOI
2408
Knowledge engineering, Training, Feature extraction, Uncertainty,
Correlation, Collaboration, deep learning
BibRef
Li, X.[Xiufang],
Jiao, L.C.[Li-Cheng],
Sun, Q.[Qigong],
Liu, F.[Fang],
Liu, X.[Xu],
Li, L.L.[Ling-Ling],
Chen, P.[Puhua],
Yang, S.Y.[Shu-Yuan],
A Category-Aware Curriculum Learning for Data-Free Knowledge
Distillation,
MultMed(26), 2024, pp. 9603-9618.
IEEE DOI
2410
Generators, Training, Knowledge engineering, Data models,
Training data, Task analysis, Monitoring, Data generation,
image classification
BibRef
Wu, J.[Jie],
Fang, L.Y.[Le-Yuan],
Yue, J.[Jun],
TAKD: Target-Aware Knowledge Distillation for Remote Sensing Scene
Classification,
CirSysVideo(34), No. 9, September 2024, pp. 8188-8200.
IEEE DOI
2410
Scene classification, Feature extraction, Computational modeling,
Training, Heating systems, Semantics, lightweight model
BibRef
Li, C.[Chuan],
Teng, X.[Xiao],
Ding, Y.[Yan],
Lan, L.[Long],
Instance-Level Scaling and Dynamic Margin-Alignment Knowledge
Distillation for Remote Sensing Image Scene Classification,
RS(16), No. 20, 2024, pp. 3853.
DOI Link
2411
BibRef
Akmel, F.[Feidu],
Meng, F.M.[Fan-Man],
Liu, M.Y.[Ming-Yu],
Zhang, R.T.[Run-Tong],
Teka, A.[Asebe],
Lemuye, E.[Elias],
Few-shot class incremental learning via prompt transfer and knowledge
distillation,
IVC(151), 2024, pp. 105251.
Elsevier DOI
2411
Knowledge distillation, Prompting, Few-shot learning, Incremental learning
BibRef
Wang, G.T.[Guang-Tai],
Huang, J.T.[Jin-Tao],
Lai, Y.Q.[Yi-Qiang],
Vong, C.M.[Chi-Man],
Dealing with partial labels by knowledge distillation,
PR(158), 2025, pp. 110965.
Elsevier DOI
2411
Partial label learning, Knowledge distillation, Over-confidence
BibRef
Salamah, A.H.[Ahmed H.],
Hamidi, S.M.[Shayan Mohajer],
Yang, E.H.[En-Hui],
A coded knowledge distillation framework for image classification
based on adaptive JPEG encoding,
PR(158), 2025, pp. 110966.
Elsevier DOI
2411
BibRef
Li, M.S.[Ming-Sheng],
Zhang, L.[Lin],
Zhu, M.Z.[Ming-Zhen],
Huang, Z.L.[Zi-Long],
Yu, G.[Gang],
Fan, J.Y.[Jia-Yuan],
Chen, T.[Tao],
Lightweight Model Pre-Training via Language Guided Knowledge
Distillation,
MultMed(26), 2024, pp. 10720-10730.
IEEE DOI
2411
Visualization, Semantics, Task analysis, Feature extraction,
Training, Computational modeling, Image segmentation,
visual semantics banks
BibRef
Wu, A.[Aming],
Yu, J.P.[Jia-Ping],
Wang, Y.X.[Yu-Xuan],
Deng, C.[Cheng],
Prototype-Decomposed Knowledge Distillation for Learning Generalized
Federated Representation,
MultMed(26), 2024, pp. 10991-11002.
IEEE DOI
2412
Prototypes, Data models, Servers, Training, Feature extraction,
Federated learning, Task analysis,
knowledge distillation
BibRef
Daultani, D.[Dinesh],
Tanaka, M.[Masayuki],
Okutomi, M.[Masatoshi],
Endo, K.[Kazuki],
Degraded Image Classification using Knowledge Distillation and Robust
Data Augmentations,
IEICE(E108-D), No. 12, December 2024, pp. 1517-1528.
WWW Link.
2501
BibRef
Nath, U.[Utkarsh],
Wang, Y.C.[Yan-Cheng],
Turaga, P.[Pavan],
Yang, Y.Z.[Ying-Zhen],
RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge
Distillation,
IJCV(132), No. 12, December 2024, pp. 5698-5717.
Springer DOI
2501
BibRef
Zou, L.[Longkun],
Zhu, W.[Wanru],
Chen, K.[Ke],
Guo, L.H.[Li-Hua],
Guo, K.L.[Kai-Ling],
Jia, K.[Kui],
Wang, Y.[Yaowei],
Boosting Cross-Domain Point Classification via Distilling Relational
Priors From 2D Transformers,
CirSysVideo(34), No. 12, December 2024, pp. 12963-12976.
IEEE DOI Code:
WWW Link.
2501
Point cloud compression, Transformers, Solid modeling, Training,
Task analysis, Shape, Unsupervised domain adaptation, point clouds,
knowledge distillation
BibRef
Sun, N.[Ning],
Xu, W.[Wei],
Liu, J.X.[Ji-Xin],
Chai, L.[Lei],
Sun, H.[Haian],
The Multimodal Scene Recognition Method Based on Self-Attention and
Distillation,
MultMedMag(31), No. 4, October 2024, pp. 25-36.
IEEE DOI
2501
Feature extraction, Training, Image recognition, Transformers,
Layout, Convolutional neural networks, Sun
BibRef
Qian, F.[Fulan],
Hong, J.C.[Jia-Cheng],
Yan, H.Q.[Huan-Qian],
Chen, H.[Hai],
Zhang, C.[Chonghao],
Su, H.[Hang],
Zhao, S.[Shu],
Empowering Object Detection: Unleashing the Potential of Decoupled
and Interactive Distillation,
ITS(26), No. 1, January 2025, pp. 544-557.
IEEE DOI
2501
Feature extraction, Object detection, Detectors, Location awareness,
Accuracy, Knowledge engineering, oriented object detection
BibRef
Lai, Y.T.[Yu-Tong],
Ning, D.J.[De-Jun],
Liu, S.P.[Shi-Peng],
KED: A Deep-Supervised Knowledge Enhancement Self-Distillation
Framework for Model Compression,
SPLetters(32), 2025, pp. 831-835.
IEEE DOI
2503
Training, Computational modeling, Knowledge engineering,
Feature extraction, Accuracy, Focusing, Data models, Data mining,
model compression
BibRef
Xu, L.X.[Li-Xiang],
Wang, Z.W.[Zhi-Wen],
Bai, L.[Lu],
Ji, S.W.[Sheng-Wei],
Ai, B.[Bing],
Wang, X.F.[Xiao-Feng],
Yu, P.S.[Philip S.],
Multi-Level Knowledge Distillation with Positional Encoding
Enhancement,
PR(163), 2025, pp. 111458.
Elsevier DOI
2503
Graph neural networks, Multi-layer perceptrons, Graph knowledge distillation,
Positional encoding, Efficient training and inference
BibRef
Yang, X.[Xi],
Kong, D.[Dechen],
Wang, N.N.[Nan-Nan],
Gao, X.B.[Xin-Bo],
Hyperbolic Insights With Knowledge Distillation for Cross-Domain
Few-Shot Learning,
IP(34), 2025, pp. 1921-1933.
IEEE DOI
2504
Metalearning, Few shot learning, Adaptation models,
Extraterrestrial measurements, Computational modeling, Training,
knowledge distillation
BibRef
Li, C.L.[Chang-Lin],
Lin, S.[Sihao],
Tang, T.[Tao],
Wang, G.[Guangrun],
Li, M.J.[Ming-Jie],
Liang, X.D.[Xiao-Dan],
Chang, X.J.[Xiao-Jun],
BossNAS Family: Block-Wisely Self-Supervised Neural Architecture
Search,
PAMI(47), No. 5, May 2025, pp. 3500-3514.
IEEE DOI
2504
Transformers, Training,
Computational modeling, Accuracy, Visualization, Correlation,
unsupervised NAS
BibRef
Li, C.L.[Chang-Lin],
Peng, J.F.[Jie-Feng],
Yuan, L.C.[Liu-Chun],
Wang, G.R.[Guang-Run],
Liang, X.D.[Xiao-Dan],
Lin, L.[Liang],
Chang, X.J.[Xiao-Jun],
Block-Wisely Supervised Neural Architecture Search With Knowledge
Distillation,
CVPR20(1986-1995)
IEEE DOI
2008
Network architecture,
Knowledge engineering, Training, DNA, Convergence, Feature extraction
BibRef
Li, C.L.[Chang-Lin],
Tang, T.[Tao],
Wang, G.[Guangrun],
Peng, J.F.[Jie-Feng],
Wang, B.[Bing],
Liang, X.D.[Xiao-Dan],
Chang, X.J.[Xiao-Jun],
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely
Self-supervised Neural Architecture Search,
ICCV21(12261-12271)
IEEE DOI
2203
Training, Visualization, Correlation, Architecture,
Computational modeling, Sociology,
Representation learning
BibRef
Wang, Z.Y.[Zhi-Yuan],
Shi, L.[Long],
Mei, Z.[Zhen],
Zhao, X.[Xiang],
Wang, Z.[Zhe],
Li, J.[Jun],
Iterative knowledge distillation and pruning for model compression in
unsupervised domain adaptation,
PR(164), 2025, pp. 111512.
Elsevier DOI
2504
Deep learning, Unsupervised domain adaptation,
Adaptive channel pruning, Transfer knowledge distillation
BibRef
Guo, Z.[Zhen],
Zhang, P.Z.[Peng-Zhou],
Liang, P.[Peng],
MFKD: Multi-dimensional feature alignment for knowledge distillation,
IVC(157), 2025, pp. 105514.
Elsevier DOI
2504
Knowledge distillation, Feature-based distillation
BibRef
Feng, K.[Kaituo],
Miao, Y.K.[Yi-Kun],
Li, C.S.[Chang-Sheng],
Yuan, Y.[Ye],
Wang, G.[Guoren],
Shared Growth of Graph Neural Networks via Prompted Free-Direction
Knowledge Distillation,
PAMI(47), No. 6, June 2025, pp. 4377-4394.
IEEE DOI
2505
Graph neural networks, Knowledge transfer, Reinforcement learning,
Training, Knowledge engineering, prompt learning
BibRef
Salas-Peña, A.[Aitor],
García-Palomares, J.C.[Juan Carlos],
Simulating Co-Evolution and Knowledge Transfer in Logistic Clusters
Using a Multi-Agent-Based Approach,
IJGI(14), No. 4, 2025, pp. 179.
DOI Link
2505
BibRef
Yang, Y.[Yang],
Wang, C.[Chao],
Gong, L.[Lei],
Wu, M.[Min],
Chen, Z.H.[Zheng-Hua],
Gao, Y.X.[Ying-Xue],
Wang, T.[Teng],
Zhou, X.[Xuehai],
Uncertainty-Aware Self-Knowledge Distillation,
CirSysVideo(35), No. 5, May 2025, pp. 4464-4478.
IEEE DOI
2505
Uncertainty, Calibration, Accuracy, Vectors, Training,
Predictive models, Smoothing methods, Artificial neural networks,
image recognition
BibRef
Chen, Y.[Yao],
Wang, J.[Jiabao],
Wang, P.[Peichao],
Zhang, R.[Rui],
Li, Y.[Yang],
Vision Mamba Distillation for Low-Resolution Fine-Grained Image
Classification,
SPLetters(32), 2025, pp. 1965-1969.
IEEE DOI
2505
Training, Feature extraction, Accuracy, Image classification,
Convolution, Visualization, Superresolution, Knowledge engineering,
low-resolution
BibRef
Zhou, L.T.[Liang-Tai],
Zhang, W.W.[Wei-Wei],
Zhang, B.H.[Bang-Hui],
Guo, Y.F.[Yu-Feng],
Wang, J.H.[Jun-Huang],
Li, X.B.[Xia-Bin],
Zhu, J.Q.[Jian-Qing],
Extensions in channel and class dimensions for attention-based
knowledge distillation,
CVIU(257), 2025, pp. 104359.
Elsevier DOI
2505
Knowledge distillation, Deep learning, Model compression
BibRef
Zhang, J.H.[Jian-Hua],
Gao, Y.[Yi],
Zhou, M.[Mian],
Liu, R.[Ruyu],
Cheng, X.[Xu],
Nikolic, S.V.[Saša V.],
Chen, S.Y.[Sheng-Yong],
SVD-KD: SVD-based hidden layer feature extraction for Knowledge
distillation,
PR(167), 2025, pp. 111721.
Elsevier DOI
2506
Knowledge distillation, Singular value decomposition,
Deep learning, Computer vision, Model compression
BibRef
Zhu, W.C.[Wen-Cheng],
Zhou, X.[Xin],
Zhu, P.F.[Peng-Fei],
Wang, Y.[Yu],
Hu, Q.H.[Qing-Hua],
CKD: Contrastive Knowledge Distillation From a Sample-Wise
Perspective,
IP(34), 2025, pp. 3578-3592.
IEEE DOI
2506
Contrastive learning, Semantics, Training,
Computational efficiency, Chronic kidney disease, Estimation,
logit alignment
BibRef
Zhang, W.W.[Wei-Wei],
Liang, P.[Peng],
Zhu, J.Q.[Jian-Qing],
Wang, J.[Junhuang],
Contrastive Deep Supervision Meets self-knowledge distillation,
JVCIR(110), 2025, pp. 104470.
Elsevier DOI
2506
Deep supervision, Contrastive learning,
Self-knowledge distillation, Image classification
BibRef
Bhattacharyya, A.[Aniket],
Tripathi, A.[Anurag],
Information Extraction from Heterogeneous Documents Without Ground
Truth Labels Using Synthetic Label Generation and Knowledge
Distillation,
WACV25(6351-6361)
IEEE DOI
2505
Training, Image quality, Visualization, Computational modeling,
Prevention and mitigation, Layout, Tail, Organizations,
visually rich document understanding
BibRef
Lee, B.[Bokyeung],
Hong, J.[Jonghwan],
Shin, H.[Hyunuk],
Ku, B.[Bonhwa],
Ko, H.S.[Han-Seok],
Dropout Connects Transformers and CNNs: Transfer General Knowledge
for Knowledge Distillation,
WACV25(8346-8355)
IEEE DOI
2505
Knowledge engineering, Training, Degradation,
Transformers, Feature extraction, classification
BibRef
Liu, J.W.[Jia-Wei],
Lam, W.[Wayne],
Zhu, Z.G.[Zhi-Gang],
Tang, H.[Hao],
SMDAF: A Scalable Sidewalk Material Data Acquisition Framework with
Bidirectional Cross-Modal Knowledge Distillation,
WACV25(2983-2992)
IEEE DOI Code:
WWW Link.
2505
Training, Learning systems, Accuracy, Navigation, Data acquisition, Refining,
Memory, Data collection, Robustness, Mobile applications, assistive technology
BibRef
Grigore, D.N.[Diana-Nicoleta],
Georgescu, M.I.[Mariana-Iuliana],
Justo, J.A.[Jon Alvarez],
Johansen, T.[Tor],
Ionescu, A.I.[Andreea Iuliana],
Ionescu, R.T.[Radu Tudor],
Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of
Vision Transformers,
WACV25(7368-7378)
IEEE DOI Code:
WWW Link.
2505
Training, Image segmentation, Pipelines, Data visualization,
Information processing, Transformers,
low-rank adaptation
BibRef
J, S.S.[Sanjay S],
J, A.[Akash],
Rajan, S.[Sreehari],
Shajahan, D.A.[Dimple A],
Sharma, C.[Charu],
Adversarial Learning Based Knowledge Distillation on 3D Point Clouds,
WACV25(2932-2941)
IEEE DOI
2505
Point cloud compression, Training, Representation learning, Deep learning,
Image segmentation, Feature extraction, Adversarial machine learning
BibRef
Adhane, G.[Gereziher],
Dehshibi, M.M.[Mohammad Mahdi],
Vetter, D.[Dennis],
Masip, D.[David],
Roig, G.[Gemma],
On Explaining Knowledge Distillation: Measuring and Visualising the
Knowledge Transfer Process,
WACV25(3467-3476)
IEEE DOI
2505
Measurement, Visualization, Plant diseases, Knowledge transfer,
explainability, knowledge distillation, visual explanation, unicam
BibRef
Xu, A.T.Y.[Alex Tian-Yi],
Wilf, A.[Alex],
Liang, P.P.[Paul Pu],
Obolenskiv, A.[Alexander],
Fried, D.[Daniel],
Morency, L.P.[Louis-Philippe],
Comparative Knowledge Distillation,
WACV25(7701-7710)
IEEE DOI
2505
Training, Correlation, Computational modeling, Large language models,
Data augmentation, Chronic kidney disease, black-box
BibRef
Su, Q.[Qing],
Ji, S.H.[Shi-Hao],
Unsqueeze [CLS] Bottleneck to Learn Rich Representations,
ECCV24(XLII: 19-37).
Springer DOI
2412
Code:
WWW Link.
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Liu, Y.[Yu],
Jia, B.X.[Bao-Xiong],
Chen, Y.X.[Yi-Xin],
Huang, S.Y.[Si-Yuan],
Slotlifter: Slot-guided Feature Lifting for Learning Object-centric
Radiance Fields,
ECCV24(IX: 270-288).
Springer DOI
2412
BibRef
Liu, B.[Bochao],
Wang, P.J.[Peng-Ju],
Ge, S.M.[Shi-Ming],
Learning Differentially Private Diffusion Models via Stochastic
Adversarial Distillation,
ECCV24(VII: 55-71).
Springer DOI
2412
BibRef
Hebbalaguppe, R.[Ramya],
Baranwal, M.[Mayank],
Anand, K.[Kartik],
Arora, C.[Chetan],
Calibration Transfer via Knowledge Distillation,
ACCV24(VIII: 210-228).
Springer DOI
2412
BibRef
Kim, S.[Soosung],
Park, Y.[Yeonhong],
Lee, H.[Hyunseung],
Yi, S.[Sungchan],
Lee, J.W.[Jae W.],
Reluifying Smooth Functions: Low-cost Knowledge Distillation to Obtain
High-performance Relu Networks,
ACCV24(VIII: 402-418).
Springer DOI
2412
BibRef
Chen, R.[Runze],
Luo, H.Y.[Hai-Yong],
Zhao, F.[Fang],
Yu, J.Z.[Jing-Ze],
Jia, Y.P.[Yu-Peng],
Wang, J.[Juan],
Ma, X.P.[Xue-Peng],
Structure-centric Robust Monocular Depth Estimation via Knowledge
Distillation,
ACCV24(IX: 123-140).
Springer DOI
2412
BibRef
Singh, A.[Aditya],
Wang, H.H.[Hao-Han],
Simple Unsupervised Knowledge Distillation With Space Similarity,
ECCV24(II: 147-164).
Springer DOI
2412
BibRef
Hao, X.S.[Xiao-Shuai],
Li, R.K.[Rui-Kai],
Zhang, H.[Hui],
Li, D.Z.[Ding-Zhe],
Yin, R.[Rong],
Jung, S.[Sangil],
Park, S.I.[Seung-In],
Yoo, B.[ByungIn],
Zhao, H.[Haimei],
Zhang, J.[Jing],
Mapdistill: Boosting Efficient Camera-based Hd Map Construction via
Camera-lidar Fusion Model Distillation,
ECCV24(III: 166-183).
Springer DOI
2412
BibRef
Wang, X.Z.[Xin-Zhou],
Wang, Y.K.[Yi-Kai],
Ye, J.L.[Jun-Liang],
Sun, F.C.[Fu-Chun],
Wang, Z.Y.[Zheng-Yi],
Wang, L.[Ling],
Liu, P.[Pengkun],
Sun, K.[Kai],
Wang, X.T.[Xin-Tong],
Xie, W.[Wende],
Liu, F.[Fangfu],
He, B.[Bin],
Animatabledreamer: Text-guided Non-rigid 3d Model Generation and
Reconstruction with Canonical Score Distillation,
ECCV24(XXV: 321-339).
Springer DOI
2412
BibRef
Wang, Y.Z.[Yu-Zhu],
Cheng, L.[Lechao],
Duan, M.[Manni],
Wang, Y.H.[Yong-Heng],
Feng, Z.[Zunlei],
Kong, S.[Shu],
Improving Knowledge Distillation via Regularizing Feature Direction and
Norm,
ECCV24(XXIV: 20-37).
Springer DOI
2412
BibRef
Sajedi, A.[Ahmad],
Khaki, S.[Samir],
Liu, L.Z.[Lucy Z.],
Amjadian, E.[Ehsan],
Lawryshyn, Y.A.[Yuri A.],
Plataniotis, K.N.[Konstantinos N.],
Data-to-model Distillation: Data-efficient Learning Framework,
ECCV24(XLIII: 438-457).
Springer DOI
2412
BibRef
Wang, H.Q.[Hui-Qun],
Bao, Y.P.[Yi-Ping],
Pan, P.[Panwang],
Li, Z.[Zeming],
Liu, X.[Xiao],
Yang, R.J.[Rui-Jie],
Huang, D.[Di],
Multi-modal Relation Distillation for Unified 3d Representation
Learning,
ECCV24(XXXIII: 364-381).
Springer DOI
2412
BibRef
Boutros, F.[Fadi],
Štruc, V.[Vitomir],
Damer, N.[Naser],
Adadistill: Adaptive Knowledge Distillation for Deep Face Recognition,
ECCV24(LV: 163-182).
Springer DOI
2412
BibRef
Yeo, S.[Sangyeop],
Jang, Y.[Yoojin],
Yoo, J.[Jaejun],
Nickel and Diming Your Gan: A Dual-method Approach to Enhancing Gan
Efficiency via Knowledge Distillation,
ECCV24(LXXXVIII: 104-121).
Springer DOI
2412
BibRef
Tang, J.L.[Jia-Liang],
Chen, S.[Shuo],
Niu, G.[Gang],
Zhu, H.Y.[Hong-Yuan],
Zhou, J.T.Y.[Joey Tian-Yi],
Gong, C.[Chen],
Sugiyama, M.[Masashi],
Direct Distillation Between Different Domains,
ECCV24(LXXX: 154-172).
Springer DOI
2412
BibRef
Harish, A.N.[Abhinav Narayan],
Heck, L.[Larry],
Hanna, J.P.[Josiah P.],
Kira, Z.[Zsolt],
Szot, A.[Andrew],
Reinforcement Learning via Auxiliary Task Distillation,
ECCV24(LXXXI: 214-230).
Springer DOI
2412
BibRef
Kim, J.[Jeongsol],
Park, G.Y.[Geon Yeong],
Ye, J.C.[Jong Chul],
Dreamsampler: Unifying Diffusion Sampling and Score Distillation for
Image Manipulation,
ECCV24(LXXXII: 398-414).
Springer DOI
2412
BibRef
Mistretta, M.[Marco],
Baldrati, A.[Alberto],
Bertini, M.[Marco],
Bagdanov, A.D.[Andrew D.],
Improving Zero-shot Generalization of Learned Prompts via Unsupervised
Knowledge Distillation,
ECCV24(LXXXIV: 459-477).
Springer DOI
2412
BibRef
Sauer, A.[Axel],
Lorenz, D.[Dominik],
Blattmann, A.[Andreas],
Rombach, R.[Robin],
Adversarial Diffusion Distillation,
ECCV24(LXXXVI: 87-103).
Springer DOI
2412
BibRef
Alldieck, T.[Thiemo],
Kolotouros, N.[Nikos],
Sminchisescu, C.[Cristian],
Score Distillation Sampling with Learned Manifold Corrective,
ECCV24(LXXXVII: 1-18).
Springer DOI
2412
BibRef
Han, C.[Cheng],
Wang, Q.F.[Qi-Fan],
Dianat, S.A.[Sohail A.],
Rabbani, M.[Majid],
Rao, R.M.[Raghuveer M.],
Fang, Y.[Yi],
Guan, Q.[Qiang],
Huang, L.[Lifu],
Liu, D.F.[Dong-Fang],
AMD: Automatic Multi-step Distillation of Large-scale Vision Models,
ECCV24(LXV: 431-450).
Springer DOI
2412
BibRef
Labe, I.[Isaac],
Issachar, N.[Noam],
Lang, I.[Itai],
Benaim, S.[Sagie],
DGD: Dynamic 3d Gaussians Distillation,
ECCV24(LXVIII: 361-378).
Springer DOI
2412
BibRef
Wang, Y.Z.[Yu-Zheng],
Chen, Z.Y.[Zhao-Yu],
Yang, D.K.[Ding-Kang],
Sun, Y.Q.[Yun-Quan],
Qi, L.[Lizhe],
Self-cooperation Knowledge Distillation for Novel Class Discovery,
ECCV24(LXX: 459-476).
Springer DOI
2412
BibRef
Jang, J.Y.[Ji-Yong],
Lee, H.[Hayeon],
Lee, Y.[Younkwan],
Disentangled Knowledge Distillation for Unified Multi-Class Anomaly
Detection,
ICIP24(312-318)
IEEE DOI
2411
Training, Location awareness, Adaptation models, Image transformation,
Scalability, Inspection, Benchmark testing, VisA
BibRef
Dong, J.H.[Jun-Hao],
Koniusz, P.[Piotr],
Chen, J.X.[Jun-Xi],
Wang, Z.J.[Z. Jane],
Ong, Y.S.[Yew-Soon],
Robust Distillation via Untargeted and Targeted Intermediate
Adversarial Samples,
CVPR24(28432-28442)
IEEE DOI
2410
Degradation, Adaptation models, Upper bound, Robustness,
Probability distribution, Distance measurement, Adversarial learning
BibRef
Wei, S.[Shicai],
Luo, C.[Chunbo],
Luo, Y.[Yang],
Scale Decoupled Distillation,
CVPR24(15975-15983)
IEEE DOI Code:
WWW Link.
2410
Correlation, Codes, Semantics, Pipelines, Benchmark testing,
Knowlegde Distillation
BibRef
Huo, F.S.[Fu-Shuo],
Xu, W.C.[Wen-Chao],
Guo, J.C.[Jing-Cai],
Wang, H.Z.[Hao-Zhao],
Guo, S.[Song],
C2KD: Bridging the Modality Gap for Cross-Modal Knowledge
Distillation,
CVPR24(16006-16015)
IEEE DOI
2410
Measurement, Knowledge transfer
BibRef
Sun, S.Q.[Shang-Quan],
Ren, W.Q.[Wen-Qi],
Li, J.Z.[Jing-Zhi],
Wang, R.[Rui],
Cao, X.C.[Xiao-Chun],
Logit Standardization in Knowledge Distillation,
CVPR24(15731-15740)
IEEE DOI
2410
Temperature distribution, Codes, Pipelines,
Toy manufacturing industry, Entropy,
Image Classification
BibRef
Zhang, Y.[Yuan],
Huang, T.[Tao],
Liu, J.[JiaMing],
Jiang, T.[Tao],
Cheng, K.[Kuan],
Zhang, S.H.[Shang-Hang],
FreeKD: Knowledge Distillation via Semantic Frequency Prompt,
CVPR24(15931-15940)
IEEE DOI
2410
Location awareness, Degradation, Sensitivity,
Frequency-domain analysis, Semantics, Pipelines, Noise, SAM
BibRef
Li, M.C.[Ming-Cheng],
Yang, D.K.[Ding-Kang],
Zhao, X.[Xiao],
Wang, S.B.[Shuai-Bing],
Wang, Y.[Yan],
Yang, K.[Kun],
Sun, M.Y.[Ming-Yang],
Kou, D.L.[Dong-Liang],
Qian, Z.Y.[Zi-Yun],
Zhang, L.H.[Li-Hua],
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment
Analysis with Incomplete Modalities,
CVPR24(12458-12468)
IEEE DOI
2410
Sentiment analysis, Correlation, Semantics, Refining, Prototypes,
Contrastive learning, Multimodal sentiment analysis,
Incomplete multimodal learning
BibRef
Wang, Y.Z.[Yu-Zheng],
Yang, D.[Dingkang],
Chen, Z.Y.[Zhao-Yu],
Liu, Y.[Yang],
Liul, S.[Siao],
Zhang, W.Q.[Wen-Qiang],
Zhang, L.H.[Li-Hua],
Qi, L.[Lizhe],
De-Confounded Data-Free Knowledge Distillation for Handling
Distribution Shifts,
CVPR24(12615-12625)
IEEE DOI
2410
Accuracy, Training data, Cause effect analysis, Data models,
Data-Free Knowledge Distillation, Causal Inference
BibRef
Daultani, D.[Dinesh],
Tanaka, M.[Masayuki],
Okutomi, M.[Masatoshi],
Endo, K.[Kazuki],
Diffusion-Based Adaptation for Classification of Unknown Degraded
Images,
NTIRE24(5982-5991)
IEEE DOI
2410
Degradation, Training, Adaptation models, Transforms,
Performance gain, Diffusion models, Transformers, ML Robustness,
Knowledge Distillation
BibRef
Yin, T.W.[Tian-Wei],
Gharbi, M.[Michaël],
Zhang, R.[Richard],
Shechtman, E.[Eli],
Durand, F.[Frédo],
Freeman, W.T.[William T.],
Park, T.[Taesung],
One-Step Diffusion with Distribution Matching Distillation,
CVPR24(6613-6623)
IEEE DOI
2410
Image quality, Computational modeling, Transforms,
Diffusion models, Generators, Hardware, image generation, generative model
BibRef
Kim, S.[Sanghwan],
Tang, H.[Hao],
Yu, F.[Fisher],
Distilling ODE Solvers of Diffusion Models into Smaller Steps,
CVPR24(9410-9419)
IEEE DOI
2410
Training, Image quality, Visualization, Limiting,
Ordinary differential equations, Diffusion models,
Knowledge distillation
BibRef
Han, K.[Keonhee],
Muhle, D.[Dominik],
Wimbauer, F.[Felix],
Cremers, D.[Daniel],
Boosting Self-Supervision for Single-View Scene Completion via
Knowledge Distillation,
CVPR24(9837-9847)
IEEE DOI
2410
Geometry, Solid modeling, Fuses, Computational modeling, Estimation,
Single-View-Reconstruction, Depth Estimation
BibRef
Ma, J.[Jing],
Xiang, X.[Xiang],
Wang, K.[Ke],
Wu, Y.C.[Yu-Chuan],
Li, Y.B.[Yong-Bin],
Aligning Logits Generatively for Principled Black-Box Knowledge
Distillation,
CVPR24(23148-23157)
IEEE DOI
2410
Computational modeling, Closed box,
Generative adversarial networks, Generators, Data models,
Black-Box Knowledge Distillation
BibRef
Tran, M.T.[Minh-Tuan],
Le, T.[Trung],
Le, X.M.[Xuan-May],
Harandi, M.[Mehrtash],
Tran, Q.H.[Quan Hung],
Phung, D.[Dinh],
NAYER: Noisy Layer Data Generation for Efficient and Effective
Data-free Knowledge Distillation,
CVPR24(23860-23869)
IEEE DOI Code:
WWW Link.
2410
Training, Knowledge engineering, Codes, Noise, Neural networks,
knowledge transfer, data-free, text embedding
BibRef
Jung, J.W.[Jae-Won],
Jang, H.[Hongsun],
Song, J.[Jaeyong],
Lee, J.H.[Jin-Ho],
PeerAiD: Improving Adversarial Distillation from a Specialized Peer
Tutor,
CVPR24(24482-24491)
IEEE DOI Code:
WWW Link.
2410
Accuracy, Codes, Computer network reliability,
Computational modeling, Neural networks, Robustness, Deep learning
BibRef
Yin, S.L.[Sheng-Lin],
Xiao, Z.[Zhen],
Song, M.X.[Ming-Xuan],
Long, J.[Jieyi],
Adversarial Distillation Based on Slack Matching and Attribution
Region Alignment,
CVPR24(24605-24614)
IEEE DOI
2410
Training, Computational modeling, Impedance matching,
Face recognition, Predictive models, Robustness
BibRef
Liu, H.[He],
Wang, Y.K.[Yi-Kai],
Liu, H.P.[Hua-Ping],
Sun, F.C.[Fu-Chun],
Yao, A.[Anbang],
Small Scale Data-Free Knowledge Distillation,
CVPR24(6008-6016)
IEEE DOI Code:
WWW Link.
2410
Training, Knowledge engineering, Semantic segmentation,
Training data, Reinforcement learning, Benchmark testing, Data-free
BibRef
Ni, J.Y.[Jian-Yuan],
Tang, H.[Hao],
Shang, Y.Z.[Yu-Zhang],
Duan, B.[Bin],
Yan, Y.[Yan],
Adaptive Cross-Architecture Mutual Knowledge Distillation,
FG24(1-5)
IEEE DOI
2408
Knowledge engineering, Training, Adaptation models, Accuracy,
Face recognition, Gesture recognition, Complex networks
BibRef
Reddy, N.[Nikhil],
Baktashmotlagh, M.[Mahsa],
Arora, C.[Chetan],
Domain-Aware Knowledge Distillation for Continual Model
Generalization,
WACV24(685-696)
IEEE DOI
2404
Adaptation models, Computational modeling, Prototypes,
Artificial neural networks, Predictive models, Synthetic data,
Autonomous Driving
BibRef
Huang, J.Q.[Jun-Qiang],
Guo, Z.C.[Zi-Chao],
Pixel-Wise Contrastive Distillation,
ICCV23(16313-16323)
IEEE DOI
2401
BibRef
Lebailly, T.[Tim],
Stegmüller, T.[Thomas],
Bozorgtabar, B.[Behzad],
Thiran, J.P.[Jean-Philippe],
Tuytelaars, T.[Tinne],
Adaptive Similarity Bootstrapping for Self-Distillation based
Representation Learning,
ICCV23(16459-16468)
IEEE DOI Code:
WWW Link.
2401
BibRef
Liu, Z.W.[Zi-Wei],
Wang, Y.T.[Yong-Tao],
Chu, X.J.[Xiao-Jie],
Dong, N.[Nan],
Qi, S.X.[Sheng-Xiang],
Ling, H.B.[Hai-Bin],
A Simple and Generic Framework for Feature Distillation via
Channel-wise Transformation,
REDLCV23(1121-1130)
IEEE DOI
2401
BibRef
Radwan, A.[Ahmed],
Shehata, M.S.[Mohamed S.],
Distilling Part-whole Hierarchical Knowledge from a Huge Pretrained
Class Agnostic Segmentation Framework,
VIPriors23(238-246)
IEEE DOI Code:
WWW Link.
2401
BibRef
Tang, J.L.[Jia-Liang],
Chen, S.[Shuo],
Niu, G.[Gang],
Sugiyama, M.[Masashi],
Gong, C.[Chen],
Distribution Shift Matters for Knowledge Distillation with Webly
Collected Images,
ICCV23(17424-17434)
IEEE DOI
2401
BibRef
Li, L.[Lujun],
Dong, P.[Peijie],
Wei, Z.[Zimian],
Yang, Y.[Ya],
Automated Knowledge Distillation via Monte Carlo Tree Search,
ICCV23(17367-17378)
IEEE DOI Code:
WWW Link.
2401
BibRef
Choi, J.Y.[Jun-Yong],
Cho, H.[Hyeon],
Cheung, S.[Seokhwa],
Hwang, W.J.[Won-Jun],
ORC: Network Group-based Knowledge Distillation using Online Role
Change,
ICCV23(17335-17344)
IEEE DOI
2401
BibRef
Yang, P.H.[Peng-Hui],
Xie, M.K.[Ming-Kun],
Zong, C.C.[Chen-Chen],
Feng, L.[Lei],
Niu, G.[Gang],
Sugiyama, M.[Masashi],
Huang, S.J.[Sheng-Jun],
Multi-Label Knowledge Distillation,
ICCV23(17225-17234)
IEEE DOI Code:
WWW Link.
2401
BibRef
Yang, Z.D.[Zhen-Dong],
Zeng, A.[Ailing],
Li, Z.[Zhe],
Zhang, T.[Tianke],
Yuan, C.[Chun],
Li, Y.[Yu],
From Knowledge Distillation to Self-Knowledge Distillation: A Unified
Approach with Normalized Loss and Customized Soft Labels,
ICCV23(17139-17148)
IEEE DOI Code:
WWW Link.
2401
BibRef
Gu, P.Y.[Pei-Yan],
Zhang, C.[Chuyu],
Xu, R.J.[Rui-Jie],
He, X.M.[Xu-Ming],
Class-relation Knowledge Distillation for Novel Class Discovery,
ICCV23(16428-16437)
IEEE DOI
2401
BibRef
Gu, Z.H.[Zhi-Hao],
Liu, L.[Liang],
Chen, X.[Xu],
Yi, R.[Ran],
Zhang, J.N.[Jiang-Ning],
Wang, Y.[Yabiao],
Wang, C.J.[Cheng-Jie],
Shu, A.[Annan],
Jiang, G.[Guannan],
Ma, L.Z.[Li-Zhuang],
Remembering Normality: Memory-guided Knowledge Distillation for
Unsupervised Anomaly Detection,
ICCV23(16355-16363)
IEEE DOI
2401
BibRef
Dong, J.F.[Jian-Feng],
Zhang, M.[Minsong],
Zhang, Z.[Zheng],
Chen, X.[Xianke],
Liu, D.[Daizong],
Qu, X.Y.[Xiao-Ye],
Wang, X.[Xun],
Liu, B.[Baolong],
Dual Learning with Dynamic Knowledge Distillation for Partially
Relevant Video Retrieval,
ICCV23(11268-11278)
IEEE DOI
2401
BibRef
Zhao, B.[Borui],
Cui, Q.[Quan],
Song, R.J.[Ren-Jie],
Liang, J.J.[Jia-Jun],
DOT: A Distillation-Oriented Trainer,
ICCV23(6166-6175)
IEEE DOI Code:
WWW Link.
2401
BibRef
Zhao, B.[Borui],
Song, R.J.[Ren-Jie],
Liang, J.J.[Jia-Jun],
Cumulative Spatial Knowledge Distillation for Vision Transformers,
ICCV23(6123-6132)
IEEE DOI Code:
WWW Link.
2401
BibRef
Gao, T.W.[Ting-Wei],
Long, R.[Rujiao],
Accumulation Knowledge Distillation for Conditional GAN Compression,
REDLCV23(1294-1303)
IEEE DOI
2401
BibRef
Bender, S.[Sidney],
Anders, C.J.[Christopher J.],
Chormai, P.[Pattarawat],
Marxfeld, H.[Heike],
Herrmann, J.[Jan],
Montavon, G.[Grégoire],
Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge
Distillation,
CVAMD23(2599-2607)
IEEE DOI
2401
BibRef
Wang, Q.[Qi],
Liu, L.[Lu],
Yu, W.X.[Wen-Xin],
Chen, S.Y.[Shi-Yu],
Gong, J.[Jun],
Chen, P.[Peng],
BCKD: Block-Correlation Knowledge Distillation,
ICIP23(3225-3229)
IEEE DOI
2312
BibRef
Sasaya, T.[Tenta],
Watanabe, T.[Takashi],
Ida, T.[Takashi],
Ono, T.[Toshiyuki],
Simple Self-Distillation Learning for Noisy Image Classification,
ICIP23(795-799)
IEEE DOI
2312
BibRef
Zhang, Y.[Yi],
Gao, Y.K.[Ying-Ke],
Zhang, H.N.[Hao-Nan],
Lei, X.Y.[Xin-Yu],
Liu, L.J.[Long-Jun],
Cross-Layer Patch Alignment and Intra-and-Inter Patch Relations for
Knowledge Distillation,
ICIP23(535-539)
IEEE DOI
2312
BibRef
Wang, C.C.[Chien-Chih],
Xu, S.Y.[Shao-Yuan],
Fu, J.M.[Jin-Miao],
Liu, Y.[Yang],
Wang, B.[Bryan],
KD-Fixmatch: Knowledge Distillation Siamese Neural Networks,
ICIP23(341-345)
IEEE DOI
2312
BibRef
Jin, Y.[Ying],
Wang, J.Q.[Jia-Qi],
Lin, D.[Dahua],
Multi-Level Logit Distillation,
CVPR23(24276-24285)
IEEE DOI
2309
BibRef
Zhmoginov, A.[Andrey],
Sandler, M.[Mark],
Miller, N.[Nolan],
Kristiansen, G.[Gus],
Vladymyrov, M.[Max],
Decentralized Learning with Multi-Headed Distillation,
CVPR23(8053-8063)
IEEE DOI
2309
BibRef
Tastan, N.[Nurbek],
Nandakumar, K.[Karthik],
CaPriDe Learning: Confidential and Private Decentralized Learning
Based on Encryption-Friendly Distillation Loss,
CVPR23(8084-8092)
IEEE DOI
2309
BibRef
Liu, G.W.[Gao-Wen],
Shang, Y.Z.[Yu-Zhang],
Yao, Y.G.[Yu-Guang],
Kompella, R.[Ramana],
Network Specialization via Feature-level Knowledge Distillation,
VOCVALC23(3368-3375)
IEEE DOI
2309
BibRef
Zhang, T.[Tianli],
Xue, M.Q.[Meng-Qi],
Zhang, J.T.[Jiang-Tao],
Zhang, H.F.[Hao-Fei],
Wang, Y.[Yu],
Cheng, L.[Lechao],
Song, J.[Jie],
Song, M.L.[Ming-Li],
Generalization Matters: Loss Minima Flattening via Parameter
Hybridization for Efficient Online Knowledge Distillation,
CVPR23(20176-20185)
IEEE DOI
2309
BibRef
Li, J.Z.[Jing-Zhi],
Guo, Z.D.[Zi-Dong],
Li, H.[Hui],
Han, S.[Seungju],
Baek, J.W.[Ji-Won],
Yang, M.[Min],
Yang, R.[Ran],
Suh, S.[Sungjoo],
Rethinking Feature-based Knowledge Distillation for Face Recognition,
CVPR23(20156-20165)
IEEE DOI
2309
BibRef
Lin, H.[Han],
Han, G.X.[Guang-Xing],
Ma, J.W.[Jia-Wei],
Huang, S.Y.[Shi-Yuan],
Lin, X.D.[Xu-Dong],
Chang, S.F.[Shih-Fu],
Supervised Masked Knowledge Distillation for Few-Shot Transformers,
CVPR23(19649-19659)
IEEE DOI
2309
BibRef
Shen, Y.Q.[Yan-Qing],
Zhou, S.P.[San-Ping],
Fu, J.W.[Jing-Wen],
Wang, R.T.[Ruo-Tong],
Chen, S.T.[Shi-Tao],
Zheng, N.N.[Nan-Ning],
StructVPR: Distill Structural Knowledge with Weighting Samples for
Visual Place Recognition,
CVPR23(11217-11226)
IEEE DOI
2309
BibRef
Xu, Q.[Qi],
Li, Y.X.[Ya-Xin],
Shen, J.R.[Jiang-Rong],
Liu, J.K.[Jian K.],
Tang, H.J.[Hua-Jin],
Pan, G.[Gang],
Constructing Deep Spiking Neural Networks from Artificial Neural
Networks with Knowledge Distillation,
CVPR23(7886-7895)
IEEE DOI
2309
BibRef
Patel, G.[Gaurav],
Mopuri, K.R.[Konda Reddy],
Qiu, Q.[Qiang],
Learning to Retain while Acquiring: Combating Distribution-Shift in
Adversarial Data-Free Knowledge Distillation,
CVPR23(7786-7794)
IEEE DOI
2309
BibRef
Chen, Y.Z.[Yi-Zhuo],
Liang, K.Z.[Kai-Zhao],
Zeng, Z.[Zhe],
Yao, S.C.[Shuo-Chao],
Shao, H.J.[Hua-Jie],
A Unified Knowledge Distillation Framework for Deep Directed
Graphical Models,
CVPR23(7795-7804)
IEEE DOI
2309
BibRef
Cui, K.W.[Kai-Wen],
Yu, Y.C.[Ying-Chen],
Zhan, F.N.[Fang-Neng],
Liao, S.C.[Sheng-Cai],
Lu, S.J.[Shi-Jian],
Xing, E.[Eric],
KD-DLGAN: Data Limited Image Generation via Knowledge Distillation,
CVPR23(3872-3882)
IEEE DOI
2309
BibRef
Xu, J.Q.[Jian-Qing],
Li, S.[Shen],
Deng, A.[Ailin],
Xiong, M.[Miao],
Wu, J.Y.[Jia-Ying],
Wu, J.X.[Jia-Xiang],
Ding, S.H.[Shou-Hong],
Hooi, B.[Bryan],
Probabilistic Knowledge Distillation of Face Ensembles,
CVPR23(3489-3498)
IEEE DOI
2309
BibRef
Guo, Z.[Ziyao],
Yan, H.[Haonan],
Li, H.[Hui],
Lin, X.D.[Xiao-Dong],
Class Attention Transfer Based Knowledge Distillation,
CVPR23(11868-11877)
IEEE DOI
2309
BibRef
Song, K.[Kaiyou],
Zhang, S.[Shan],
Luo, Z.[Zimeng],
Wang, T.[Tong],
Xie, J.[Jin],
Semantics-Consistent Feature Search for Self-Supervised Visual
Representation Learning,
ICCV23(16053-16062)
IEEE DOI
2401
BibRef
Song, K.[Kaiyou],
Xie, J.[Jin],
Zhang, S.[Shan],
Luo, Z.[Zimeng],
Multi-Mode Online Knowledge Distillation for Self-Supervised Visual
Representation Learning,
CVPR23(11848-11857)
IEEE DOI
2309
BibRef
Yu, S.K.[Shi-Kang],
Chen, J.C.[Jia-Chen],
Han, H.[Hu],
Jiang, S.Q.[Shu-Qiang],
Data-Free Knowledge Distillation via Feature Exchange and Activation
Region Constraint,
CVPR23(24266-24275)
IEEE DOI
2309
BibRef
Shrivastava, A.[Aman],
Qi, Y.J.[Yan-Jun],
Ordonez, V.[Vicente],
Estimating and Maximizing Mutual Information for Knowledge
Distillation,
FaDE-TCV23(48-57)
IEEE DOI
2309
BibRef
Gao, L.[Lei],
Gao, H.[Hui],
Feature Decoupled Knowledge Distillation via Spatial Pyramid Pooling,
ACCV22(VI:732-745).
Springer DOI
2307
BibRef
Lv, Y.[Yuan],
Xu, Y.J.[Ya-Jing],
Wang, S.[Shusen],
Ma, Y.J.[Ying-Jian],
Wang, D.[Dengke],
Continuous Self-Study: Scene Graph Generation with Self-Knowledge
Distillation and Spatial Augmentation,
ACCV22(V:297-315).
Springer DOI
2307
BibRef
Liu, Y.F.[Yu-Fan],
Cao, J.J.[Jia-Jiong],
Li, B.[Bing],
Hu, W.M.[Wei-Ming],
Ding, J.T.[Jing-Ting],
Li, L.[Liang],
Cross-architecture Knowledge Distillation,
ACCV22(V:179-195).
Springer DOI
2307
BibRef
Lee, H.[Hojung],
Lee, J.S.[Jong-Seok],
Rethinking Online Knowledge Distillation with Multi-Exits,
ACCV22(VI:408-424).
Springer DOI
2307
BibRef
Wang, L.Y.[Li-Yun],
Rhodes, A.[Anthony],
Feng, W.C.[Wu-Chi],
Class Specialized Knowledge Distillation,
ACCV22(II:391-408).
Springer DOI
2307
BibRef
Li, W.[Wei],
Shao, S.T.[Shi-Tong],
Liu, W.Y.[Wei-Yan],
Qiu, Z.M.[Zi-Ming],
Zhu, Z.H.[Zhi-Hao],
Huan, W.[Wei],
What Role Does Data Augmentation Play in Knowledge Distillation?,
ACCV22(II:507-525).
Springer DOI
2307
BibRef
Feng, P.[Ping],
Zhang, H.Y.[Han-Yun],
Sun, Y.Y.[Ying-Ying],
Tang, Z.J.[Zhen-Jun],
Lightweight Image Hashing Based on Knowledge Distillation and Optimal
Transport for Face Retrieval,
MMMod23(II: 423-434).
Springer DOI
2304
BibRef
Ambekar, S.[Sameer],
Tafuro, M.[Matteo],
Ankit, A.[Ankit],
van der Mast, D.[Diego],
Alence, M.[Mark],
Athanasiadis, C.[Christos],
Skdcgn: Source-free Knowledge Distillation of Counterfactual Generative
Networks Using cgans,
VIPriors22(679-693).
Springer DOI
2304
BibRef
Lebailly, T.[Tim],
Tuytelaars, T.[Tinne],
Global-Local Self-Distillation for Visual Representation Learning,
WACV23(1441-1450)
IEEE DOI
2302
Training, Representation learning, Visualization, Codes, Coherence,
Task analysis, Algorithms: Machine learning architectures,
and algorithms (including transfer)
BibRef
Choi, H.J.[Hong-Jun],
Jeon, E.S.[Eun Som],
Shukla, A.[Ankita],
Turaga, P.[Pavan],
Understanding the Role of Mixup in Knowledge Distillation:
An Empirical Study,
WACV23(2318-2327)
IEEE DOI
2302
Knowledge engineering, Training, Interpolation, Codes,
Transfer learning, Robustness,
adversarial attack and defense methods
BibRef
Jacob, G.M.[Geethu Miriam],
Agarwal, V.[Vishal],
Stenger, B.[Björn],
Online Knowledge Distillation for Multi-task Learning,
WACV23(2358-2367)
IEEE DOI
2302
Training, Knowledge engineering, Semantic segmentation,
Computational modeling, Estimation, Benchmark testing
BibRef
Chen, W.C.[Wei-Chi],
Chu, W.T.[Wei-Ta],
SSSD: Self-Supervised Self Distillation,
WACV23(2769-2776)
IEEE DOI
2302
Visualization, Computational modeling, Clustering algorithms,
Self-supervised learning, Feature extraction, Data models,
visual reasoning
BibRef
Mu, M.[Michael],
Bhattacharjee, S.D.[Sreyasee Das],
Yuan, J.S.[Jun-Song],
Self-Supervised Distilled Learning for Multi-modal Misinformation
Identification,
WACV23(2818-2827)
IEEE DOI
2302
Representation learning, Training data, Predictive models,
Streaming media, Semisupervised learning, Multitasking,
Vision + language and/or other modalities
BibRef
Jang, J.[Jiho],
Kim, S.[Seonhoon],
Yoo, K.[Kiyoon],
Kong, C.[Chaerin],
Kim, J.[Jangho],
Kwak, N.[Nojun],
Self-Distilled Self-supervised Representation Learning,
WACV23(2828-2838)
IEEE DOI
2302
Representation learning, Protocols, Codes, Statistical analysis,
Self-supervised learning, Transformers,
and algorithms (including transfer)
BibRef
Nguyen-Duc, T.[Thanh],
Le, T.[Trung],
Zhao, H.[He],
Cai, J.F.[Jian-Fei],
Phung, D.[Dinh],
Adversarial local distribution regularization for knowledge
distillation,
WACV23(4670-4679)
IEEE DOI
2302
Perturbation methods, Algorithms: Adversarial learning,
adversarial attack and defense methods
BibRef
Wu, Y.[Yong],
Chanda, S.[Shekhor],
Hosseinzadeh, M.[Mehrdad],
Liu, Z.[Zhi],
Wang, Y.[Yang],
Few-Shot Learning of Compact Models via Task-Specific Meta
Distillation,
WACV23(6254-6263)
IEEE DOI
2302
Training, Adaptation models, Computational modeling,
Benchmark testing, Servers, visual reasoning
BibRef
Hosseinzadeh, M.[Mehrdad],
Wang, Y.[Yang],
Few-Shot Personality-Specific Image Captioning via Meta-Learning,
CRV23(320-327)
IEEE DOI
2406
Metalearning, Adaptation models, Protocols, Benchmark testing,
Data processing, Standards, Robots, Image Captioning, few-shot learning
BibRef
Iwata, S.[Sachi],
Minami, S.[Soma],
Hirakawa, T.[Tsubasa],
Yamashita, T.[Takayoshi],
Fujiyoshi, H.[Hironobu],
Refining Design Spaces in Knowledge Distillation for Deep
Collaborative Learning,
ICPR22(2371-2377)
IEEE DOI
2212
Analytical models, Federated learning, Refining,
Task analysis, Knowledge transfer
BibRef
Wang, C.F.[Chao-Fei],
Zhang, S.W.[Shao-Wei],
Song, S.[Shiji],
Huang, G.[Gao],
Learn From the Past: Experience Ensemble Knowledge Distillation,
ICPR22(4736-4743)
IEEE DOI
2212
Knowledge engineering, Training, Adaptation models, Costs,
Standards, Knowledge transfer
BibRef
Tzelepi, M.[Maria],
Symeonidis, C.[Charalampos],
Nikolaidis, N.[Nikos],
Tefas, A.[Anastasios],
Multilayer Online Self-Acquired Knowledge Distillation,
ICPR22(4822-4828)
IEEE DOI
2212
Training, Computational modeling, Pipelines, Estimation,
Nonhomogeneous media, Probability distribution
BibRef
Xu, Y.F.[Yi-Fan],
Shamsolmoali, P.[Pourya],
Granger, E.[Eric],
Nicodeme, C.[Claire],
Gardes, L.[Laurent],
Yang, J.[Jie],
TransVLAD: Multi-Scale Attention-Based Global Descriptors for Visual
Geo-Localization,
WACV23(2839-2848)
IEEE DOI
2302
Visualization, Codes, Computational modeling, Image retrieval,
Self-supervised learning, Transformers,
and un-supervised learning)
BibRef
Xu, Y.F.[Yi-Fan],
Shamsolmoali, P.[Pourya],
Yang, J.[Jie],
Weak-supervised Visual Geo-localization via Attention-based Knowledge
Distillation,
ICPR22(1815-1821)
IEEE DOI
2212
Knowledge engineering, Training, Visualization, Image matching,
Image retrieval, Lighting, Benchmark testing
BibRef
Baek, K.[Kyungjune],
Lee, S.[Seungho],
Shim, H.J.[Hyun-Jung],
Learning from Better Supervision: Self-distillation for Learning with
Noisy Labels,
ICPR22(1829-1835)
IEEE DOI
2212
Training, Deep learning, Filtering, Neural networks,
Predictive models, Data collection, Benchmark testing
BibRef
Chen, D.[Dingyao],
Tan, H.[Huibin],
Lan, L.[Long],
Zhang, X.[Xiang],
Liang, T.Y.[Tian-Yi],
Luo, Z.G.[Zhi-Gang],
Frustratingly Easy Knowledge Distillation via Attentive Similarity
Matching,
ICPR22(2357-2363)
IEEE DOI
2212
Knowledge engineering, Dimensionality reduction,
Cross layer design, Semantics, Mobile handsets,
Pattern recognition
BibRef
Shen, L.[Lulan],
Amara, I.[Ibtihel],
Li, R.F.[Ruo-Feng],
Meyer, B.[Brett],
Gross, W.[Warren],
Clark, J.J.[James J.],
Fast Fine-Tuning Using Curriculum Domain Adaptation,
CRV23(296-303)
IEEE DOI
2406
Training, Performance evaluation, Adaptation models, Pipelines,
Artificial neural networks, Task analysis,
fine-tuning
BibRef
Amara, I.[Ibtihel],
Ziaeefard, M.[Maryam],
Meyer, B.H.[Brett H.],
Gross, W.[Warren],
Clark, J.J.[James J.],
CES-KD: Curriculum-based Expert Selection for Guided Knowledge
Distillation,
ICPR22(1901-1907)
IEEE DOI
2212
Knowledge engineering, Performance evaluation, Bridges, Art, Education
BibRef
Yang, Z.[Zhou],
Dong, W.S.[Wei-Sheng],
Li, X.[Xin],
Wu, J.J.[Jin-Jian],
Li, L.[Leida],
Shi, G.M.[Guang-Ming],
Self-Feature Distillation with Uncertainty Modeling for Degraded Image
Recognition,
ECCV22(XXIV:552-569).
Springer DOI
2211
BibRef
Shen, Z.Q.[Zhi-Qiang],
Xing, E.[Eric],
A Fast Knowledge Distillation Framework for Visual Recognition,
ECCV22(XXIV:673-690).
Springer DOI
2211
BibRef
Yang, C.G.[Chuan-Guang],
An, Z.[Zhulin],
Zhou, H.[Helong],
Cai, L.H.[Lin-Hang],
Zhi, X.[Xiang],
Wu, J.W.[Ji-Wen],
Xu, Y.J.[Yong-Jun],
Zhang, Q.[Qian],
MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition,
ECCV22(XXIV:534-551).
Springer DOI
2211
BibRef
Xu, S.[Sheng],
Li, Y.J.[Yan-Jing],
Zeng, B.[Bohan],
Ma, T.[Teli],
Zhang, B.C.[Bao-Chang],
Cao, X.B.[Xian-Bin],
Gao, P.[Peng],
Lü, J.[Jinhu],
IDa-Det: An Information Discrepancy-Aware Distillation for 1-Bit
Detectors,
ECCV22(XI:346-361).
Springer DOI
2211
BibRef
Gao, Y.T.[Yu-Ting],
Zhuang, J.X.[Jia-Xin],
Lin, S.H.[Shao-Hui],
Cheng, H.[Hao],
Sun, X.[Xing],
Li, K.[Ke],
Shen, C.H.[Chun-Hua],
DisCo: Remedying Self-supervised Learning on Lightweight Models with
Distilled Contrastive Learning,
ECCV22(XXVI:237-253).
Springer DOI
2211
BibRef
Liu, H.[Hao],
Ye, M.[Mang],
Improving Self-supervised Lightweight Model Learning via Hard-Aware
Metric Distillation,
ECCV22(XXXI:295-311).
Springer DOI
2211
BibRef
Deng, X.Q.[Xue-Qing],
Sun, D.W.[Da-Wei],
Newsam, S.[Shawn],
Wang, P.[Peng],
DistPro: Searching a Fast Knowledge Distillation Process via Meta
Optimization,
ECCV22(XXXIV:218-235).
Springer DOI
2211
BibRef
Deng, X.[Xiang],
Zheng, J.[Jian],
Zhang, Z.F.[Zhong-Fei],
Personalized Education: Blind Knowledge Distillation,
ECCV22(XXXIV:269-285).
Springer DOI
2211
BibRef
Shu, H.Q.[Hong-Qiao],
Two Distillation Perspectives Based on Tanimoto Coefficient,
ICIP22(1311-1315)
IEEE DOI
2211
Training, Length measurement, Task analysis, Knowledge transfer,
Knowledge distillation, Tanimoto similarity matrix, Tanimoto coefficient
BibRef
Li, C.X.[Chen-Xin],
Lin, M.[Mingbao],
Ding, Z.Y.[Zhi-Yuan],
Lin, N.[Nie],
Zhuang, Y.H.[Yi-Hong],
Huang, Y.[Yue],
Ding, X.H.[Xing-Hao],
Cao, L.J.[Liu-Juan],
Knowledge Condensation Distillation,
ECCV22(XI:19-35).
Springer DOI
2211
BibRef
Yang, Z.D.[Zhen-Dong],
Li, Z.[Zhe],
Shao, M.Q.[Ming-Qi],
Shi, D.C.[Da-Chuan],
Yuan, Z.H.[Ze-Huan],
Yuan, C.[Chun],
Masked Generative Distillation,
ECCV22(XI:53-69).
Springer DOI
2211
BibRef
Liang, J.J.[Jia-Jun],
Li, L.[Linze],
Bing, Z.D.[Zhao-Dong],
Zhao, B.R.[Bo-Rui],
Tang, Y.[Yao],
Lin, B.[Bo],
Fan, H.Q.[Hao-Qiang],
Efficient One Pass Self-distillation with Zipf's Label Smoothing,
ECCV22(XI:104-119).
Springer DOI
2211
BibRef
Park, J.[Jinhyuk],
No, A.[Albert],
Prune Your Model Before Distill It,
ECCV22(XI:120-136).
Springer DOI
2211
BibRef
Qian, B.[Biao],
Wang, Y.[Yang],
Yin, H.Z.[Hong-Zhi],
Hong, R.C.[Ri-Chang],
Wang, M.[Meng],
Switchable Online Knowledge Distillation,
ECCV22(XI:449-466).
Springer DOI
2211
BibRef
Okamoto, N.[Naoki],
Hirakawa, T.[Tsubasa],
Yamashita, T.[Takayoshi],
Fujiyoshi, H.[Hironobu],
Deep Ensemble Learning by Diverse Knowledge Distillation for
Fine-Grained Object Classification,
ECCV22(XI:502-518).
Springer DOI
2211
BibRef
Jang, Y.K.[Young Kyun],
Gu, G.[Geonmo],
Ko, B.[Byungsoo],
Kang, I.[Isaac],
Cho, N.I.[Nam Ik],
Deep Hash Distillation for Image Retrieval,
ECCV22(XIV:354-371).
Springer DOI
2211
BibRef
Nguyen, D.[Dang],
Gupta, S.I.[Sun-Il],
Do, K.[Kien],
Venkatesh, S.[Svetha],
Black-Box Few-Shot Knowledge Distillation,
ECCV22(XXI:196-211).
Springer DOI
2211
BibRef
Oh, Y.J.[Yu-Jin],
Ye, J.C.[Jong Chul],
CXR Segmentation by AdaIN-Based Domain Adaptation and Knowledge
Distillation,
ECCV22(XXI:627-643).
Springer DOI
2211
BibRef
Lee, K.[Kyungmoon],
Kim, S.[Sungyeon],
Kwak, S.[Suha],
Cross-domain Ensemble Distillation for Domain Generalization,
ECCV22(XXV:1-20).
Springer DOI
2211
BibRef
Li, J.C.[Jun-Cheng],
Yang, H.[Hanhui],
Yi, Q.[Qiaosi],
Fang, F.[Faming],
Gao, G.W.[Guang-Wei],
Zeng, T.Y.[Tie-Yong],
Zhang, G.X.[Gui-Xu],
Multiple Degradation and Reconstruction Network for Single Image
Denoising via Knowledge Distillation,
NTIRE22(557-566)
IEEE DOI
2210
Degradation, Knowledge engineering, Computational modeling,
Resists, Image restoration, Noise measurement
BibRef
He, R.F.[Rui-Fei],
Sun, S.Y.[Shu-Yang],
Yang, J.H.[Ji-Han],
Bai, S.[Song],
Qi, X.J.[Xiao-Juan],
Knowledge Distillation as Efficient Pre-training: Faster Convergence,
Higher Data-efficiency, and Better Transferability,
CVPR22(9151-9161)
IEEE DOI
2210
Training, Codes, Computational modeling,
Data models,
Efficient learning and inferences
BibRef
Xie, P.T.[Peng-Tao],
Du, X.F.[Xue-Feng],
Performance-Aware Mutual Knowledge Distillation for Improving Neural
Architecture Search,
CVPR22(11912-11922)
IEEE DOI
2210
Computational modeling,
Optimization, Deep learning architectures and techniques
BibRef
Shen, Y.Q.[Yi-Qing],
Xu, L.[Liwu],
Yang, Y.Z.[Yu-Zhe],
Li, Y.Q.[Ya-Qian],
Guo, Y.D.[Yan-Dong],
Self-Distillation from the Last Mini-Batch for Consistency
Regularization,
CVPR22(11933-11942)
IEEE DOI
2210
Training, Codes, Computer network reliability, Memory management,
Network architecture, Benchmark testing,
Machine learning
BibRef
Chen, X.N.[Xia-Ning],
Cao, Q.[Qiong],
Zhong, Y.J.[Yu-Jie],
Zhang, J.[Jing],
Gao, S.H.[Sheng-Hua],
Tao, D.C.[Da-Cheng],
DearKD: Data-Efficient Early Knowledge Distillation for Vision
Transformers,
CVPR22(12042-12052)
IEEE DOI
2210
Training, Deep learning, Computational modeling,
Optimization methods, Transformers,
Optimization methods
BibRef
Yang, C.G.[Chuan-Guang],
Zhou, H.[Helong],
An, Z.[Zhulin],
Jiang, X.[Xue],
Xu, Y.J.[Yong-Jun],
Zhang, Q.[Qian],
Cross-Image Relational Knowledge Distillation for Semantic
Segmentation,
CVPR22(12309-12318)
IEEE DOI
2210
Image segmentation, Correlation, Codes, Shape, Semantics,
Efficient learning and inferences,
grouping and shape analysis
BibRef
Lin, S.[Sihao],
Xie, H.W.[Hong-Wei],
Wang, B.[Bing],
Yu, K.C.[Kai-Cheng],
Chang, X.J.[Xiao-Jun],
Liang, X.D.[Xiao-Dan],
Wang, G.[Gang],
Knowledge Distillation via the Target-aware Transformer,
CVPR22(10905-10914)
IEEE DOI
2210
Knowledge engineering, Deep learning, Codes, Semantics,
Neural networks, retrieval
BibRef
Li, T.H.[Tian-Hao],
Wang, L.M.[Li-Min],
Wu, G.S.[Gang-Shan],
Self Supervision to Distillation for Long-Tailed Visual Recognition,
ICCV21(610-619)
IEEE DOI
2203
Training, Representation learning, Deep learning, Visualization,
Image recognition, Head, Semantics, Recognition and classification,
Representation learning
BibRef
Fang, Z.Y.[Zhi-Yuan],
Wang, J.F.[Jian-Feng],
Hu, X.W.[Xiao-Wei],
Wang, L.J.[Li-Juan],
Yang, Y.Z.[Ye-Zhou],
Liu, Z.C.[Zi-Cheng],
Compressing Visual-linguistic Model via Knowledge Distillation,
ICCV21(1408-1418)
IEEE DOI
2203
Knowledge engineering, Visualization, Adaptation models, Detectors,
Mean square error methods, Transformers, Vision + language,
Vision applications and systems
BibRef
Tejankar, A.[Ajinkya],
Koohpayegani, S.A.[Soroush Abbasi],
Pillai, V.[Vipin],
Favaro, P.[Paolo],
Pirsiavash, H.[Hamed],
ISD: Self-Supervised Learning by Iterative Similarity Distillation,
ICCV21(9589-9598)
IEEE DOI
2203
Codes, Transfer learning, Iterative methods, Task analysis,
Standards, Representation learning, Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Zhou, S.[Sheng],
Wang, Y.C.[Yu-Cheng],
Chen, D.F.[De-Fang],
Chen, J.W.[Jia-Wei],
Wang, X.[Xin],
Wang, C.[Can],
Bu, J.J.[Jia-Jun],
Distilling Holistic Knowledge with Graph Neural Networks,
ICCV21(10367-10376)
IEEE DOI
2203
Knowledge engineering, Correlation, Codes, Knowledge based systems,
Benchmark testing, Feature extraction,
BibRef
Shang, Y.Z.[Yu-Zhang],
Duan, B.[Bin],
Zong, Z.L.[Zi-Liang],
Nie, L.Q.[Li-Qiang],
Yan, Y.[Yan],
Lipschitz Continuity Guided Knowledge Distillation,
ICCV21(10655-10664)
IEEE DOI
2203
Knowledge engineering, Training, Image segmentation, Codes,
NP-hard problem, Neural networks,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Li, Z.[Zheng],
Ye, J.W.[Jing-Wen],
Song, M.L.[Ming-Li],
Huang, Y.[Ying],
Pan, Z.[Zhigeng],
Online Knowledge Distillation for Efficient Pose Estimation,
ICCV21(11720-11730)
IEEE DOI
2203
Heating systems, Computational modeling, Pose estimation,
Benchmark testing, Complexity theory, Knowledge transfer,
Efficient training and inference methods
BibRef
Dai, R.[Rui],
Das, S.[Srijan],
Bremond, F.[François],
Learning an Augmented RGB Representation with Cross-Modal Knowledge
Distillation for Action Detection,
ICCV21(13033-13044)
IEEE DOI
2203
Training, Focusing, Streaming media, Real-time systems,
Task analysis, Action and behavior recognition,
Vision + other modalities
BibRef
Xiang, S.[Sitao],
Gu, Y.M.[Yu-Ming],
Xiang, P.D.[Peng-Da],
Chai, M.L.[Meng-Lei],
Li, H.[Hao],
Zhao, Y.J.[Ya-Jie],
He, M.M.[Ming-Ming],
DisUnknown: Distilling Unknown Factors for Disentanglement Learning,
ICCV21(14790-14799)
IEEE DOI
2203
Training, Scalability, Benchmark testing, Generators, Task analysis,
Image and video synthesis, Adversarial learning, Neural generative models
BibRef
Diomataris, M.[Markos],
Gkanatsios, N.[Nikolaos],
Pitsikalis, V.[Vassilis],
Maragos, P.[Petros],
Grounding Consistency: Distilling Spatial Common Sense for Precise
Visual Relationship Detection,
ICCV21(15891-15900)
IEEE DOI
2203
Measurement, Visualization, Grounding, Triples (Data structure),
Image edge detection, Predictive models,
Visual reasoning and logical representation
BibRef
Zheng, H.[Heliang],
Yang, H.[Huan],
Fu, J.L.[Jian-Long],
Zha, Z.J.[Zheng-Jun],
Luo, J.B.[Jie-Bo],
Learning Conditional Knowledge Distillation for Degraded-Reference
Image Quality Assessment,
ICCV21(10222-10231)
IEEE DOI
2203
Measurement, Image quality, Training, Knowledge engineering,
Computational modeling, Semantics, Image restoration,
Low-level and physics-based vision
BibRef
Zheng, H.L.[He-Liang],
Fu, J.L.[Jian-Long],
Zha, Z.J.[Zheng-Jun],
Luo, J.B.[Jie-Bo],
Looking for the Devil in the Details: Learning Trilinear Attention
Sampling Network for Fine-Grained Image Recognition,
CVPR19(5007-5016).
IEEE DOI
2002
BibRef
Liu, L.[Li],
Huang, Q.L.[Qing-Le],
Lin, S.[Sihao],
Xie, H.W.[Hong-Wei],
Wang, B.[Bing],
Chang, X.J.[Xiao-Jun],
Liang, X.D.[Xiao-Dan],
Exploring Inter-Channel Correlation for Diversity-preserved Knowledge
Distillation,
ICCV21(8251-8260)
IEEE DOI
2203
Knowledge engineering, Image segmentation, Correlation, Costs,
Semantics, Graphics processing units,
grouping and shape
BibRef
Wang, H.[Hong],
Deng, Y.F.[Yue-Fan],
Yoo, S.[Shinjae],
Ling, H.B.[Hai-Bin],
Lin, Y.W.[Yue-Wei],
AGKD-BML: Defense Against Adversarial Attack by Attention Guided
Knowledge Distillation and Bi-directional Metric Learning,
ICCV21(7638-7647)
IEEE DOI
2203
Training, Deep learning, Codes, Computational modeling,
Neural networks, Bidirectional control, Adversarial learning,
BibRef
Li, C.C.[Cheng-Cheng],
Wang, Z.[Zi],
Qi, H.R.[Hai-Rong],
Online Knowledge Distillation by Temporal-Spatial Boosting,
WACV22(3482-3491)
IEEE DOI
2202
Training, Knowledge engineering,
Benchmark testing, Boosting, Noise measurement,
Deep Learning Deep Learning -> Efficient Training and
Inference Methods for Networks
BibRef
Zheng, Z.Z.[Zhen-Zhu],
Peng, X.[Xi],
Self-Guidance: Improve Deep Neural Network Generalization via
Knowledge Distillation,
WACV22(3451-3460)
IEEE DOI
2202
Training, Deep learning, Knowledge engineering, Measurement,
Visualization, Image recognition, Neural networks,
Learning and Optimization
BibRef
Zhang, H.[Heng],
Fromont, E.[Elisa],
Lefevre, S.[Sébastien],
Avignon, B.[Bruno],
Low-cost Multispectral Scene Analysis with Modality Distillation,
WACV22(3331-3340)
IEEE DOI
2202
Knowledge engineering, Image analysis, Image resolution, Semantics,
Neural networks, Thermal sensors, Predictive models,
Vision Systems and Applications
BibRef
Vo, D.M.[Duc Minh],
Sugimoto, A.[Akihiro],
Nakayama, H.[Hideki],
PPCD-GAN: Progressive Pruning and Class-Aware Distillation for
Large-Scale Conditional GANs Compression,
WACV22(1422-1430)
IEEE DOI
2202
Training, Image coding, Neural network compression,
GANs
BibRef
Meng, Z.[Ze],
Yao, X.[Xin],
Sun, L.F.[Li-Feng],
Multi-Task Distillation:
Towards Mitigating the Negative Transfer in Multi-Task Learning,
ICIP21(389-393)
IEEE DOI
2201
Training, Degradation, Image processing, Optimization methods,
Benchmark testing, Turning, Multi-task Learning,
Multi-objective optimization
BibRef
Tang, Q.[Qiankun],
Xu, X.G.[Xiao-Gang],
Wang, J.[Jun],
Differentiable Dynamic Channel Association for Knowledge Distillation,
ICIP21(414-418)
IEEE DOI
2201
Image coding, Computational modeling, Network architecture,
Probabilistic logic, Computational efficiency, Task analysis,
weighted distillation
BibRef
Tran, V.[Vinh],
Wang, Y.[Yang],
Zhang, Z.K.[Ze-Kun],
Hoai, M.[Minh],
Knowledge Distillation for Human Action Anticipation,
ICIP21(2518-2522)
IEEE DOI
2201
Training, Knowledge engineering, Image processing, Semantics,
Neural networks, Training data
BibRef
Tran, V.[Vinh],
Balasubramanian, N.[Niranjan],
Hoai, M.[Minh],
Progressive Knowledge Distillation for Early Action Recognition,
ICIP21(2583-2587)
IEEE DOI
2201
Knowledge engineering, Training, Recurrent neural networks,
Image recognition, Training data, Semisupervised learning
BibRef
Rotman, M.[Michael],
Wolf, L.B.[Lior B.],
Natural Statistics of Network Activations and Implications for
Knowledge Distillation,
ICIP21(399-403)
IEEE DOI
2201
Deep learning, Knowledge engineering, Image recognition,
Correlation, Semantics, Benchmark testing, Knowledge Distillation,
Image Statistics
BibRef
Zhu, J.G.[Jin-Guo],
Tang, S.X.[Shi-Xiang],
Chen, D.P.[Da-Peng],
Yu, S.J.[Shi-Jie],
Liu, Y.K.[Ya-Kun],
Rong, M.Z.[Ming-Zhe],
Yang, A.[Aijun],
Wang, X.H.[Xiao-Hua],
Complementary Relation Contrastive Distillation,
CVPR21(9256-9265)
IEEE DOI
2111
Benchmark testing, Mutual information
BibRef
Jung, S.[Sangwon],
Lee, D.G.[Dong-Gyu],
Park, T.[Taeeon],
Moon, T.[Taesup],
Fair Feature Distillation for Visual Recognition,
CVPR21(12110-12119)
IEEE DOI
2111
Visualization, Systematics,
Computational modeling, Face recognition, Predictive models,
Prediction algorithms
BibRef
Ghosh, P.[Pallabi],
Saini, N.[Nirat],
Davis, L.S.[Larry S.],
Shrivastava, A.[Abhinav],
Learning Graphs for Knowledge Transfer with Limited Labels,
CVPR21(11146-11156)
IEEE DOI
2111
Training, Visualization, Convolution,
Semisupervised learning, Benchmark testing
BibRef
Huang, Z.[Zhen],
Shen, X.[Xu],
Xing, J.[Jun],
Liu, T.L.[Tong-Liang],
Tian, X.M.[Xin-Mei],
Li, H.Q.[Hou-Qiang],
Deng, B.[Bing],
Huang, J.Q.[Jian-Qiang],
Hua, X.S.[Xian-Sheng],
Revisiting Knowledge Distillation:
An Inheritance and Exploration Framework,
CVPR21(3578-3587)
IEEE DOI
2111
Training, Learning systems, Knowledge engineering, Deep learning,
Neural networks, Reinforcement learning
BibRef
Salehi, M.[Mohammadreza],
Sadjadi, N.[Niousha],
Baselizadeh, S.[Soroosh],
Rohban, M.H.[Mohammad H.],
Rabiee, H.R.[Hamid R.],
Multiresolution Knowledge Distillation for Anomaly Detection,
CVPR21(14897-14907)
IEEE DOI
2111
Training, Location awareness, Knowledge engineering,
Image resolution, Task analysis
BibRef
Yang, L.[Lehan],
Xu, K.[Kele],
Cross Modality Knowledge Distillation for Multi-modal Aerial View
Object Classification,
NTIRE21(382-387)
IEEE DOI
2109
Training, Speckle, Feature extraction, Radar polarimetry,
Data models, Robustness
BibRef
Bhat, P.[Prashant],
Arani, E.[Elahe],
Zonooz, B.[Bahram],
Distill on the Go: Online knowledge distillation in self-supervised
learning,
LLID21(2672-2681)
IEEE DOI
2109
Annotations,
Performance gain, Benchmark testing
BibRef
Nayak, G.K.[Gaurav Kumar],
Mopuri, K.R.[Konda Reddy],
Chakraborty, A.[Anirban],
Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge
Distillation,
WACV21(1429-1437)
IEEE DOI
2106
Training, Visualization, Sensitivity, Computational modeling,
Semantics, Neural networks, Training data
BibRef
Lee, J.[Jongmin],
Jeong, Y.[Yoonwoo],
Kim, S.[Seungwook],
Min, J.[Juhong],
Cho, M.[Minsu],
Learning to Distill Convolutional Features into Compact Local
Descriptors,
WACV21(897-907)
IEEE DOI
2106
Location awareness, Visualization, Image matching, Semantics,
Benchmark testing, Feature extraction, Robustness
BibRef
Arani, E.[Elahe],
Sarfraz, F.[Fahad],
Zonooz, B.[Bahram],
Noise as a Resource for Learning in Knowledge Distillation,
WACV21(3128-3137)
IEEE DOI
2106
Training, Uncertainty, Neuroscience, Collaboration,
Collaborative work, Brain modeling, Probabilistic logic
BibRef
Kothandaraman, D.[Divya],
Nambiar, A.[Athira],
Mittal, A.[Anurag],
Domain Adaptive Knowledge Distillation for Driving Scene Semantic
Segmentation,
WACVW21(134-143) Autonomous Vehicle Vision
IEEE DOI
2105
Knowledge engineering, Adaptation models, Image segmentation,
Semantics, Memory management
BibRef
Kushawaha, R.K.[Ravi Kumar],
Kumar, S.[Saurabh],
Banerjee, B.[Biplab],
Velmurugan, R.[Rajbabu],
Distilling Spikes: Knowledge Distillation in Spiking Neural Networks,
ICPR21(4536-4543)
IEEE DOI
2105
Knowledge engineering, Training, Image coding,
Computational modeling, Artificial neural networks,
Hardware
BibRef
Sarfraz, F.[Fahad],
Arani, E.[Elahe],
Zonooz, B.[Bahram],
Knowledge Distillation Beyond Model Compression,
ICPR21(6136-6143)
IEEE DOI
2105
Training, Knowledge engineering, Neural networks,
Network architecture, Collaborative work, Robustness
BibRef
Ahmed, W.[Waqar],
Zunino, A.[Andrea],
Morerio, P.[Pietro],
Murino, V.[Vittorio],
Compact CNN Structure Learning by Knowledge Distillation,
ICPR21(6554-6561)
IEEE DOI
2105
Training, Learning systems, Knowledge engineering,
Network architecture, Predictive models
BibRef
Ma, J.X.[Jia-Xin],
Yonetani, R.[Ryo],
Iqbal, Z.[Zahid],
Adaptive Distillation for Decentralized Learning from Heterogeneous
Clients,
ICPR21(7486-7492)
IEEE DOI
2105
Learning systems, Adaptation models, Visualization,
Biomedical equipment, Medical services, Collaborative work, Data models
BibRef
Tsunashima, H.[Hideki],
Kataoka, H.[Hirokatsu],
Yamato, J.J.[Jun-Ji],
Chen, Q.[Qiu],
Morishima, S.[Shigeo],
Adversarial Knowledge Distillation for a Compact Generator,
ICPR21(10636-10643)
IEEE DOI
2105
Training, Image resolution, MIMICs, Generators
BibRef
Kim, J.H.[Jang-Ho],
Hyun, M.S.[Min-Sung],
Chung, I.[Inseop],
Kwak, N.[Nojun],
Feature Fusion for Online Mutual Knowledge Distillation,
ICPR21(4619-4625)
IEEE DOI
2105
Neural networks, Education, Performance gain
BibRef
Mitsuno, K.[Kakeru],
Nomura, Y.[Yuichiro],
Kurita, T.[Takio],
Channel Planting for Deep Neural Networks using Knowledge
Distillation,
ICPR21(7573-7579)
IEEE DOI
2105
Training, Knowledge engineering,
Heuristic algorithms, Neural networks,
Network architecture
BibRef
Cui, W.,
Li, X.,
Huang, J.,
Wang, W.,
Wang, S.,
Chen, J.,
Substitute Model Generation for Black-Box Adversarial Attack Based on
Knowledge Distillation,
ICIP20(648-652)
IEEE DOI
2011
Perturbation methods, Task analysis, Training,
Computational modeling, Approximation algorithms,
black-box models
BibRef
Xu, K.R.[Kun-Ran],
Rui, L.[Lai],
Li, Y.S.[Yi-Shi],
Gu, L.[Lin],
Feature Normalized Knowledge Distillation for Image Classification,
ECCV20(XXV:664-680).
Springer DOI
2011
BibRef
Yang, Y.,
Qiu, J.,
Song, M.,
Tao, D.,
Wang, X.,
Distilling Knowledge From Graph Convolutional Networks,
CVPR20(7072-7081)
IEEE DOI
2008
Knowledge engineering, Task analysis,
Computational modeling, Computer science, Training, Neural networks
BibRef
Yun, J.S.[Ju-Seung],
Kim, B.[Byungjoo],
Kim, J.[Junmo],
Weight Decay Scheduling and Knowledge Distillation for Active Learning,
ECCV20(XXVI:431-447).
Springer DOI
2011
BibRef
Wei, L.H.[Long-Hui],
Xiao, A.[An],
Xie, L.X.[Ling-Xi],
Zhang, X.P.[Xiao-Peng],
Chen, X.[Xin],
Tian, Q.[Qi],
Circumventing Outliers of Autoaugment with Knowledge Distillation,
ECCV20(III:608-625).
Springer DOI
2012
BibRef
Walawalkar, D.[Devesh],
Shen, Z.Q.[Zhi-Qiang],
Savvides, M.[Marios],
Online Ensemble Model Compression Using Knowledge Distillation,
ECCV20(XIX:18-35).
Springer DOI
2011
BibRef
Xiang, L.Y.[Liu-Yu],
Ding, G.G.[Gui-Guang],
Han, J.G.[Jun-Gong],
Learning From Multiple Experts: Self-paced Knowledge Distillation for
Long-tailed Classification,
ECCV20(V:247-263).
Springer DOI
2011
BibRef
Zhou, B.[Brady],
Kalra, N.[Nimit],
Krähenbühl, P.[Philipp],
Domain Adaptation Through Task Distillation,
ECCV20(XXVI:664-680).
Springer DOI
2011
BibRef
Li, Z.[Zheng],
Huang, Y.[Ying],
Chen, D.F.[De-Fang],
Luo, T.R.[Tian-Ren],
Cai, N.[Ning],
Pan, Z.G.[Zhi-Geng],
Online Knowledge Distillation via Multi-branch Diversity Enhancement,
ACCV20(IV:318-333).
Springer DOI
2103
BibRef
Ye, H.J.[Han-Jia],
Lu, S.[Su],
Zhan, D.C.[De-Chuan],
Distilling Cross-Task Knowledge via Relationship Matching,
CVPR20(12393-12402)
IEEE DOI
2008
Task analysis, Neural networks, Training, Knowledge engineering,
Predictive models, Stochastic processes, Temperature measurement
BibRef
Yao, A.B.[An-Bang],
Sun, D.W.[Da-Wei],
Knowledge Transfer via Dense Cross-layer Mutual-distillation,
ECCV20(XV:294-311).
Springer DOI
2011
BibRef
Yue, K.Y.[Kai-Yu],
Deng, J.F.[Jiang-Fan],
Zhou, F.[Feng],
Matching Guided Distillation,
ECCV20(XV:312-328).
Springer DOI
2011
BibRef
Wang, D.Y.[De-Yu],
Wen, D.C.[Dong-Chao],
Liu, J.J.[Jun-Jie],
Tao, W.[Wei],
Chen, T.W.[Tse-Wei],
Osa, K.[Kinya],
Kato, M.[Masami],
Fully Supervised and Guided Distillation for One-stage Detectors,
ACCV20(III:171-188).
Springer DOI
2103
BibRef
Liu, B.L.[Ben-Lin],
Rao, Y.M.[Yong-Ming],
Lu, J.W.[Ji-Wen],
Zhou, J.[Jie],
Hsieh, C.J.[Cho-Jui],
Metadistiller:
Network Self-boosting via Meta-learned Top-down Distillation,
ECCV20(XIV:694-709).
Springer DOI
2011
BibRef
Choi, Y.,
Choi, J.,
El-Khamy, M.,
Lee, J.,
Data-Free Network Quantization With Adversarial Knowledge
Distillation,
EDLCV20(3047-3057)
IEEE DOI
2008
Generators, Quantization (signal), Training,
Computational modeling, Data models, Machine learning, Data privacy
BibRef
de Vieilleville, F.,
Lagrange, A.,
Ruiloba, R.,
May, S.,
Towards Distillation of Deep Neural Networks for Satellite On-board
Image Segmentation,
ISPRS20(B2:1553-1559).
DOI Link
2012
BibRef
Wang, X.B.[Xiao-Bo],
Fu, T.Y.[Tian-Yu],
Liao, S.C.[Sheng-Cai],
Wang, S.[Shuo],
Lei, Z.[Zhen],
Mei, T.[Tao],
Exclusivity-Consistency Regularized Knowledge Distillation for Face
Recognition,
ECCV20(XXIV:325-342).
Springer DOI
2012
BibRef
Guan, Y.S.[Yu-Shuo],
Zhao, P.Y.[Peng-Yu],
Wang, B.X.[Bing-Xuan],
Zhang, Y.X.[Yuan-Xing],
Yao, C.[Cong],
Bian, K.G.[Kai-Gui],
Tang, J.[Jian],
Differentiable Feature Aggregation Search for Knowledge Distillation,
ECCV20(XVII:469-484).
Springer DOI
2011
BibRef
Gu, J.D.[Jin-Dong],
Wu, Z.L.[Zhi-Liang],
Tresp, V.[Volker],
Introspective Learning by Distilling Knowledge from Online
Self-explanation,
ACCV20(IV:36-52).
Springer DOI
2103
BibRef
Guo, Q.S.[Qiu-Shan],
Wang, X.J.[Xin-Jiang],
Wu, Y.C.[Yi-Chao],
Yu, Z.P.[Zhi-Peng],
Liang, D.[Ding],
Hu, X.L.[Xiao-Lin],
Luo, P.[Ping],
Online Knowledge Distillation via Collaborative Learning,
CVPR20(11017-11026)
IEEE DOI
2008
Knowledge engineering, Training, Collaborative work,
Perturbation methods, Collaboration, Neural networks, Logic gates
BibRef
Li, T.,
Li, J.,
Liu, Z.,
Zhang, C.,
Few Sample Knowledge Distillation for Efficient Network Compression,
CVPR20(14627-14635)
IEEE DOI
2008
Training, Tensile stress, Knowledge engineering, Convolution,
Neural networks, Computational modeling, Standards
BibRef
Seddik, M.E.A.,
Essafi, H.,
Benzine, A.,
Tamaazousti, M.,
Lightweight Neural Networks From PCA LDA Based Distilled Dense Neural
Networks,
ICIP20(3060-3064)
IEEE DOI
2011
Neural networks, Principal component analysis,
Computational modeling, Training, Machine learning,
Lightweight Networks
BibRef
Tung, F.[Fred],
Mori, G.[Greg],
Similarity-Preserving Knowledge Distillation,
ICCV19(1365-1374)
IEEE DOI
2004
learning (artificial intelligence), neural nets,
semantic networks, Task analysis
BibRef
Zhang, M.Y.[Man-Yuan],
Song, G.L.[Guang-Lu],
Zhou, H.[Hang],
Liu, Y.[Yu],
Discriminability Distillation in Group Representation Learning,
ECCV20(X:1-19).
Springer DOI
2011
BibRef
Jin, X.[Xiao],
Peng, B.Y.[Bao-Yun],
Wu, Y.C.[Yi-Chao],
Liu, Y.[Yu],
Liu, J.H.[Jia-Heng],
Liang, D.[Ding],
Yan, J.J.[Jun-Jie],
Hu, X.L.[Xiao-Lin],
Knowledge Distillation via Route Constrained Optimization,
ICCV19(1345-1354)
IEEE DOI
2004
face recognition, image classification,
learning (artificial intelligence), neural nets, optimisation,
Neural networks
BibRef
Mullapudi, R.T.,
Chen, S.,
Zhang, K.,
Ramanan, D.,
Fatahalian, K.,
Online Model Distillation for Efficient Video Inference,
ICCV19(3572-3581)
IEEE DOI
2004
convolutional neural nets, image segmentation,
inference mechanisms, learning (artificial intelligence),
Cameras
BibRef
Peng, B.,
Jin, X.,
Li, D.,
Zhou, S.,
Wu, Y.,
Liu, J.,
Zhang, Z.,
Liu, Y.,
Correlation Congruence for Knowledge Distillation,
ICCV19(5006-5015)
IEEE DOI
2004
correlation methods, face recognition, image classification,
learning (artificial intelligence), instance-level information,
Knowledge transfer
BibRef
Vongkulbhisal, J.[Jayakorn],
Vinayavekhin, P.[Phongtharin],
Visentini-Scarzanella, M.[Marco],
Unifying Heterogeneous Classifiers With Distillation,
CVPR19(3170-3179).
IEEE DOI
2002
BibRef
Park, W.[Wonpyo],
Kim, D.J.[Dong-Ju],
Lu, Y.[Yan],
Cho, M.[Minsu],
Relational Knowledge Distillation,
CVPR19(3962-3971).
IEEE DOI
2002
BibRef
Liu, Y.F.[Yu-Fan],
Cao, J.J.[Jia-Jiong],
Li, B.[Bing],
Yuan, C.F.[Chun-Feng],
Hu, W.M.[Wei-Ming],
Li, Y.X.[Yang-Xi],
Duan, Y.Q.[Yun-Qiang],
Knowledge Distillation via Instance Relationship Graph,
CVPR19(7089-7097).
IEEE DOI
2002
BibRef
Ahn, S.S.[Sung-Soo],
Hu, S.X.[Shell Xu],
Damianou, A.[Andreas],
Lawrence, N.D.[Neil D.],
Dai, Z.W.[Zhen-Wen],
Variational Information Distillation for Knowledge Transfer,
CVPR19(9155-9163).
IEEE DOI
2002
BibRef
Minami, S.[Soma],
Yamashita, T.[Takayoshi],
Fujiyoshi, H.[Hironobu],
Gradual Sampling Gate for Bidirectional Knowledge Distillation,
MVA19(1-6)
DOI Link
1911
Transfer knowledge from large pre-trained network to smaller one.
data compression, learning (artificial intelligence),
neural nets, gradual sampling gate,
Power markets
BibRef
Chen, W.C.[Wei-Chun],
Chang, C.C.[Chia-Che],
Lee, C.R.[Che-Rung],
Knowledge Distillation with Feature Maps for Image Classification,
ACCV18(III:200-215).
Springer DOI
1906
BibRef
Hou, S.H.[Sai-Hui],
Pan, X.Y.[Xin-Yu],
Loy, C.C.[Chen Change],
Wang, Z.L.[Zi-Lei],
Lin, D.H.[Da-Hua],
Lifelong Learning via Progressive Distillation and Retrospection,
ECCV18(III: 452-467).
Springer DOI
1810
BibRef
Pintea, S.L.[Silvia L.],
Liu, Y.[Yue],
van Gemert, J.C.[Jan C.],
Recurrent Knowledge Distillation,
ICIP18(3393-3397)
IEEE DOI
1809
small network learns from larger network.
Computational modeling, Memory management, Training, Color,
Convolution, Road transportation, Knowledge distillation,
recurrent layers
BibRef
Lee, S.H.[Seung Hyun],
Kim, D.H.[Dae Ha],
Song, B.C.[Byung Cheol],
Self-supervised Knowledge Distillation Using Singular Value
Decomposition,
ECCV18(VI: 339-354).
Springer DOI
1810
BibRef
Yim, J.,
Joo, D.,
Bae, J.,
Kim, J.,
A Gift from Knowledge Distillation: Fast Optimization, Network
Minimization and Transfer Learning,
CVPR17(7130-7138)
IEEE DOI
1711
Feature extraction, Knowledge engineering,
Knowledge transfer, Optimization, Training
BibRef
Gupta, S.[Saurabh],
Hoffman, J.[Judy],
Malik, J.[Jitendra],
Cross Modal Distillation for Supervision Transfer,
CVPR16(2827-2836)
IEEE DOI
1612
BibRef
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Knowledge Distillation for Object Detection .