Tajbakhsh, N.,
Shin, J.Y.,
Gurudu, S.R.,
Hurst, R.T.,
Kendall, C.B.,
Gotway, M.B.,
Liang, J.,
Convolutional Neural Networks for Medical Image Analysis:
Full Training or Fine Tuning?,
MedImg(35), No. 5, May 2016, pp. 1299-1312.
IEEE DOI
1605
Biomedical imaging
BibRef
Shin, J.Y.,
Tajbakhsh, N.,
Hurst, R.T.,
Kendall, C.B.,
Liang, J.,
Automating Carotid Intima-Media Thickness Video Interpretation with
Convolutional Neural Networks,
CVPR16(2526-2535)
IEEE DOI
1612
BibRef
Zhou, Z.,
Shin, J.,
Zhang, L.,
Gurudu, S.,
Gotway, M.B.,
Liang, J.,
Fine-Tuning Convolutional Neural Networks for Biomedical Image
Analysis: Actively and Incrementally,
CVPR17(4761-4772)
IEEE DOI
1711
Biomedical imaging, Entropy, Labeling, Machine learning,
Noise measurement, Training
BibRef
Zheng, Y.[Yan],
Wang, R.G.[Rong-Gui],
Yang, J.[Juan],
Xue, L.X.[Li-Xia],
Hu, M.[Min],
Principal characteristic networks for few-shot learning,
JVCIR(59), 2019, pp. 563-573.
Elsevier DOI
1903
Few-shot learning, Principal characteristic,
Mixture loss function, Embedding network, Fine-tuning
BibRef
Yang, H.D.[Hao-Dong],
Kang, X.Y.[Xin-Yue],
Liu, L.[Long],
Liu, Y.J.[Yu-Jiang],
Huang, Z.L.[Zhong-Ling],
SAR-HUB: Pre-Training, Fine-Tuning, and Explaining,
RS(15), No. 23, 2023, pp. 5534.
DOI Link
2312
BibRef
Li, X.[Xiao],
Fang, M.[Min],
Li, H.[Haikun],
Wu, J.Q.[Jin-Qiao],
Zero shot learning based on class visual prototypes and semantic
consistency,
PRL(135), 2020, pp. 368-374.
Elsevier DOI
2006
Zero shot learning, Semantic consistency,
Class visual prototypes, Shared sparse graph
BibRef
Li, X.[Xiao],
Fang, M.[Min],
Li, H.[Haikun],
Wu, J.Q.[Jin-Qiao],
Learning discriminative and meaningful samples for generalized zero
shot classification,
SP:IC(87), 2020, pp. 115920.
Elsevier DOI
2007
Generalized zero shot classification,
Generative adversarial network, Class consistency, Semantic consistency
BibRef
Li, X.[Xiao],
Fang, M.[Min],
Chen, B.[Bo],
Generalized zero-shot classification via iteratively generating and
selecting unseen samples,
SP:IC(92), 2021, pp. 116115.
Elsevier DOI
2101
Generalized zero shot classification, Generative adversarial network,
Unseen visual prototypes
BibRef
Zhai, Z.B.[Zhi-Bo],
Li, X.[Xiao],
Chang, Z.H.[Zhong-Hao],
Center-VAE with discriminative and semantic-relevant fine-tuning
features for generalized zero-shot learning,
SP:IC(111), 2023, pp. 116897.
Elsevier DOI
2301
Generalized zero-shot learning, VAE, Center loss,
Semantic-relevant, Fine-tuning
BibRef
Pang, S.[Shanmin],
He, X.[Xin],
Hao, W.Y.[Wen-Yu],
Long, Y.[Yang],
Feature fine-tuning and attribute representation transformation for
zero-shot learning,
CVIU(236), 2023, pp. 103811.
Elsevier DOI
2310
Generalized zero-shot learning, Generative adversarial networks,
Data distribution, Information asymmetric problem
BibRef
Han, C.[Cheng],
Wang, Q.F.[Qi-Fan],
Cui, Y.M.[Yi-Ming],
Cao, Z.W.[Zhi-Wen],
Wang, W.G.[Wen-Guan],
Qi, S.Y.[Si-Yuan],
Liu, D.F.[Dong-Fang],
E2VPT: An Effective and Efficient Approach for Visual Prompt Tuning,
ICCV23(17445-17456)
IEEE DOI Code:
WWW Link.
2401
BibRef
Jie, S.[Shibo],
Wang, H.Q.[Hao-Qing],
Deng, Z.H.[Zhi-Hong],
Revisiting the Parameter Efficiency of Adapters from the Perspective
of Precision Redundancy,
ICCV23(17171-17180)
IEEE DOI Code:
WWW Link.
2401
BibRef
Yang, Y.Q.[Yun-Qiao],
Huang, L.K.[Long-Kai],
Wei, Y.[Ying],
Concept-wise Fine-tuning Matters in Preventing Negative Transfer,
ICCV23(18707-18717)
IEEE DOI
2401
BibRef
Li, H.[Hao],
Fowlkes, C.[Charless],
Yang, H.[Hao],
Dabeer, O.[Onkar],
Tu, Z.W.[Zhuo-Wen],
Soatto, S.[Stefano],
Guided Recommendation for Model Fine-Tuning,
CVPR23(3633-3642)
IEEE DOI
2309
BibRef
Wang, J.Y.[Jun-Yang],
Xu, Y.H.[Yuan-Hong],
Hu, J.[Juhua],
Yan, M.[Ming],
Sang, J.[Jitao],
Qian, Q.[Qi],
Improved Visual Fine-tuning with Natural Language Supervision,
ICCV23(11865-11875)
IEEE DOI Code:
WWW Link.
2401
BibRef
Tian, J.J.[Jun-Jiao],
Dai, X.L.[Xiao-Liang],
Ma, C.Y.[Chih-Yao],
He, Z.C.[Ze-Cheng],
Liu, Y.C.[Yen-Cheng],
Kira, Z.[Zsolt],
Trainable Projected Gradient Method for Robust Fine-Tuning,
CVPR23(7836-7845)
IEEE DOI
2309
BibRef
Zhou, N.[Nan],
Chen, J.X.[Jia-Xin],
Huang, D.[Di],
DR-Tune: Improving Fine-tuning of Pretrained Visual Models by
Distribution Regularization with Semantic Calibration,
ICCV23(1547-1556)
IEEE DOI Code:
WWW Link.
2401
BibRef
Singh, A.[Aaditya],
Sarangmath, K.[Kartik],
Chattopadhyay, P.[Prithvijit],
Hoffman, J.[Judy],
Benchmarking Low-Shot Robustness to Natural Distribution Shifts,
ICCV23(16186-16196)
IEEE DOI
2401
fine-tuning
BibRef
Xie, E.[Enze],
Yao, L.W.[Le-Wei],
Shi, H.[Han],
Liu, Z.[Zhili],
Zhou, D.[Daquan],
Liu, Z.Q.[Zhao-Qiang],
Li, J.W.[Jia-Wei],
Li, Z.G.[Zhen-Guo],
DiffFit: Unlocking Transferability of Large Diffusion Models via
Simple Parameter-Efficient Fine-Tuning,
ICCV23(4207-4216)
IEEE DOI
2401
BibRef
Liu, T.Y.[Tian Yu],
Soatto, S.[Stefano],
Tangent Model Composition for Ensembling and Continual Fine-tuning,
ICCV23(18630-18640)
IEEE DOI Code:
WWW Link.
2401
BibRef
Tao, R.[Ran],
Chen, H.[Hao],
Savvides, M.[Marios],
Boosting Transductive Few-Shot Fine-tuning with Margin-based
Uncertainty Weighting and Probability Regularization,
CVPR23(15752-15761)
IEEE DOI
2309
BibRef
Shen, T.Y.[Tian-Yi],
Lee, C.[Chonghan],
Narayanan, V.[Vijaykrishnan],
Multi-Exit Vision Transformer with Custom Fine-Tuning for
Fine-Grained Image Recognition,
ICIP23(2830-2834)
IEEE DOI
2312
BibRef
Liu, Z.Q.[Zi-Quan],
Xu, Y.[Yi],
Ji, X.Y.[Xiang-Yang],
Chan, A.B.[Antoni B.],
TWINS: A Fine-Tuning Framework for Improved Transferability of
Adversarial Robustness and Generalization,
CVPR23(16436-16446)
IEEE DOI
2309
BibRef
Park, H.[Hojin],
Park, J.[Jaewoo],
Teoh, A.B.J.[Andrew Beng Jin],
Open-Set Face Identification on Few-Shot Gallery by Fine-Tuning,
ICPR22(1026-1032)
IEEE DOI
2212
WWW Link. Face recognition, Source coding, Computational modeling,
Benchmark testing, Task analysis
BibRef
Xu, H.[Hang],
Kang, N.[Ning],
Zhang, G.[Gengwei],
Xie, C.L.[Chuan-Long],
Liang, X.D.[Xiao-Dan],
Li, Z.G.[Zhen-Guo],
NASOA:
Towards Faster Task-Oriented Online Fine-Tuning with a Zoo of Models,
ICCV21(5077-5086)
IEEE DOI
2203
Training, Adaptation models, Cloud computing, Schedules,
Computational modeling, Graphics processing units,
Vision applications and systems
BibRef
Chamand, B.[Benjamin],
Risser-Maroix, O.[Olivier],
Kurtz, C.[Camille],
Joly, P.[Philippe],
Loménie, N.[Nicolas],
Fine-Tune Your Classifier: Finding Correlations with Temperature,
ICIP22(2766-2770)
IEEE DOI
2211
Temperature measurement, Training, Measurement,
Temperature distribution, Correlation, Computational modeling, cross-entropy
BibRef
Shon, H.[Hyounguk],
Lee, J.[Janghyeon],
Kim, S.H.[Seung Hwan],
Kim, J.[Junmo],
DLCFT: Deep Linear Continual Fine-Tuning for General Incremental
Learning,
ECCV22(XXXIII:513-529).
Springer DOI
2211
BibRef
Jie, S.[Shibo],
Deng, Z.H.[Zhi-Hong],
Li, Z.H.[Zi-Heng],
Alleviating Representational Shift for Continual Fine-tuning,
CLVision22(3809-3818)
IEEE DOI
2210
Training, Pattern recognition, Task analysis, Testing
BibRef
Hu, S.X.[Shell Xu],
Li, D.[Da],
Stühmer, J.[Jan],
Kim, M.Y.[Min-Young],
Hospedales, T.M.[Timothy M.],
Pushing the Limits of Simple Pipelines for Few-Shot Learning:
External Data and Fine-Tuning Make a Difference,
CVPR22(9058-9067)
IEEE DOI
2210
Training, Pipelines, Transfer learning,
Benchmark testing, Transformers,
Self- semi- meta- unsupervised learning
BibRef
Wortsman, M.[Mitchell],
Ilharco, G.[Gabriel],
Kim, J.W.[Jong Wook],
Li, M.[Mike],
Kornblith, S.[Simon],
Roelofs, R.[Rebecca],
Lopes, R.G.[Raphael Gontijo],
Hajishirzi, H.[Hannaneh],
Farhadi, A.[Ali],
Namkoong, H.[Hongseok],
Schmidt, L.[Ludwig],
Robust fine-tuning of zero-shot models,
CVPR22(7949-7961)
IEEE DOI
2210
Computational modeling, Computer network reliability,
Transfer learning, Neural networks, Robustness, Data models, Machine learning
BibRef
Zhang, L.[Lin],
Shen, L.[Li],
Ding, L.[Liang],
Tao, D.C.[Da-Cheng],
Duan, L.Y.[Ling-Yu],
Fine-tuning Global Model via Data-Free Knowledge Distillation for
Non-IID Federated Learning,
CVPR22(10164-10173)
IEEE DOI
2210
Training, Privacy, Distance learning, Computational modeling,
Machine learning, Collaborative work, Generators,
Transfer/low-shot/long-tail learning
BibRef
Cao, Y.T.[Yu-Tong],
Shi, Y.[Ye],
Yu, B.S.[Bao-Sheng],
Wang, J.Y.[Jing-Ya],
Tao, D.C.[Da-Cheng],
Knowledge-Aware Federated Active Learning with Non-IID Data,
ICCV23(22222-22232)
IEEE DOI Code:
WWW Link.
2401
BibRef
Suzuki, S.[Satoshi],
Takeda, S.[Shoichiro],
Tanida, R.[Ryuichi],
Kimata, H.[Hideaki],
Shouno, H.[Hayaru],
Knowledge Transferred Fine-Tuning for Anti-Aliased Convolutional
Neural Network in Data-Limited Situation,
ICIP21(864-868)
IEEE DOI
2201
Knowledge engineering, Training, Image recognition, Training data,
Convolutional neural networks, Knowledge transfer,
data-limited situation
BibRef
Achille, A.[Alessandro],
Golatkar, A.[Aditya],
Ravichandran, A.[Avinash],
Polito, M.[Marzia],
Soatto, S.[Stefano],
LQF: Linear Quadratic Fine-Tuning,
CVPR21(15724-15734)
IEEE DOI
2111
Deep learning, Training data,
Computer architecture, Robustness, Pattern recognition, Task analysis
BibRef
Sadhukhan, R.,
Saha, A.,
Mukhopadhyay, J.,
Patra, A.,
Knowledge Distillation Inspired Fine-Tuning Of Tucker Decomposed CNNS
and Adversarial Robustness Analysis,
ICIP20(1876-1880)
IEEE DOI
2011
Robustness, Knowledge engineering, Convolution, Tensile stress,
Neural networks, Perturbation methods, Acceleration,
Adversarial Robustness
BibRef
Li, Q.[Qi],
Mai, L.[Long],
Alcorn, M.A.[Michael A.],
Nguyen, A.[Anh],
A Cost-effective Method for Improving and Re-purposing Large,
Pre-trained GANs by Fine-tuning Their Class-embeddings,
ACCV20(IV:526-541).
Springer DOI
2103
BibRef
Tanveer, M.S.[Muhammad Suhaib],
Khan, M.U.K.[Muhammad Umar Karim],
Kyung, C.M.[Chong-Min],
Fine-Tuning DARTS for Image Classification,
ICPR21(4789-4796)
IEEE DOI
2105
Microprocessors, Image classification
BibRef
Protopapadakis, E.[Eftychios],
Doulamis, A.[Anastasios],
Doulamis, N.[Nikolaos],
Maltezos, E.[Evangelos],
Semi-supervised Fine-tuning for Deep Learning Models in Remote Sensing
Applications,
ISVC20(I:719-730).
Springer DOI
2103
BibRef
Guo, Y.H.[Yun-Hui],
Shi, H.H.[Hong-Hui],
Kumar, A.[Abhishek],
Grauman, K.[Kristen],
Rosing, T.[Tajana],
Feris, R.S.[Rogerio S.],
SpotTune: Transfer Learning Through Adaptive Fine-Tuning,
CVPR19(4800-4809).
IEEE DOI
2002
BibRef
Gui, L.Y.,
Gui, L.,
Wang, Y.X.,
Morency, L.P.,
Moura, J.M.F.,
Factorized Convolutional Networks:
Unsupervised Fine-Tuning for Image Clustering,
WACV18(1205-1214)
IEEE DOI
1806
convolution, feedforward neural nets, image recognition,
image representation, matrix decomposition, pattern clustering,
Tuning
BibRef
Ge, W.,
Yu, Y.,
Borrowing Treasures from the Wealthy:
Deep Transfer Learning through Selective Joint Fine-Tuning,
CVPR17(10-19)
IEEE DOI
1711
Kernel, Machine learning, Neural networks, Training data, Visualization
BibRef
Chu, B.[Brian],
Madhavan, V.[Vashisht],
Beijbom, O.[Oscar],
Hoffman, J.[Judy],
Darrell, T.J.[Trevor J.],
Best Practices for Fine-Tuning Visual Classifiers to New Domains,
TASKCV16(III: 435-442).
Springer DOI
1611
Fine tuned generic to specific domain.
BibRef
Rosa, G.[Gustavo],
Papa, J.[João],
Marana, A.[Aparecido],
Scheirer, W.[Walter],
Cox, D.[David],
Fine-Tuning Convolutional Neural Networks Using Harmony Search,
CIARP15(683-690).
Springer DOI
1511
BibRef
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Constraint Based Matching .