14.1.8.7 Adversarial Networks for Transfer Learning, Domain Adaption

Chapter Contents (Back)
Transfer Learning. GAN Transfer Learning. Domain Adaptation. Adversarial Networks.
See also Zero-Shot Learning.
See also Data Hiding, Steganography, Adversarial Networks, Convolutional Networks, Deep Learning. GAN
See also Open Set, Open World Recongnition.

Hoffman, J.[Judy], Rodner, E.[Erik], Donahue, J.[Jeff], Kulis, B.[Brian], Saenko, K.[Kate],
Asymmetric and Category Invariant Feature Transformations for Domain Adaptation,
IJCV(109), No. 1-2, August 2014, pp. 28-41.
Springer DOI 1407
BibRef

Tzeng, E., Hoffman, J.[Judy], Saenko, K.[Kate], Darrell, T.J.[Trevor J.],
Adversarial Discriminative Domain Adaptation,
CVPR17(2962-2971)
IEEE DOI 1711
BibRef
Earlier: A2, A4, A3, Only:
Continuous Manifold Based Adaptation for Evolving Visual Domains,
CVPR14(867-874)
IEEE DOI 1409
Adaptation models, Image reconstruction, Standards, Training, Visualization BibRef

Tzeng, E., Hoffman, J., Darrell, T.J., Saenko, K.,
Simultaneous Deep Transfer Across Domains and Tasks,
ICCV15(4068-4076)
IEEE DOI 1602
Adaptation models BibRef

Hoffman, J.[Judy], Kulis, B.[Brian], Darrell, T.J.[Trevor J.], Saenko, K.[Kate],
Discovering Latent Domains for Multisource Domain Adaptation,
ECCV12(II: 702-715).
Springer DOI 1210
BibRef

Kulis, B.[Brian], Saenko, K.[Kate], Darrell, T.J.[Trevor J.],
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms,
CVPR11(1785-1792).
IEEE DOI 1106
Training is not adequate. Domain adaptation. BibRef

Saenko, K.[Kate], Kulis, B.[Brian], Fritz, M.[Mario], Darrell, T.J.[Trevor J.],
Adapting Visual Category Models to New Domains,
ECCV10(IV: 213-226).
Springer DOI 1009
BibRef

Donahue, J.[Jeff], Hoffman, J.[Judy], Rodner, E.[Erik], Saenko, K.[Kate], Darrell, T.J.[Trevor J.],
Semi-supervised Domain Adaptation with Instance Constraints,
CVPR13(668-675)
IEEE DOI 1309
domain adaptation; visual recognition BibRef

Shao, M.[Ming], Kit, D.[Dmitry], Fu, Y.[Yun],
Generalized Transfer Subspace Learning Through Low-Rank Constraint,
IJCV(109), No. 1-2, August 2014, pp. 74-93.
Springer DOI 1407
Using existing data for transfer to new domains. BibRef

Ding, Z.M.[Zheng-Ming], Fu, Y.[Yun],
Robust Transfer Metric Learning for Image Classification,
IP(26), No. 2, February 2017, pp. 660-670.
IEEE DOI 1702
computational complexity BibRef

Ding, Z.M.[Zheng-Ming], Fu, Y.[Yun],
Deep Domain Generalization With Structured Low-Rank Constraint,
IP(27), No. 1, January 2018, pp. 304-313.
IEEE DOI 1712
learning (artificial intelligence), neural nets, common knowledge, consistent knowledge, low-rank constraint BibRef

Ding, Z.M.[Zheng-Ming], Shao, M.[Ming], Fu, Y.[Yun],
Generative Zero-Shot Learning via Low-Rank Embedded Semantic Dictionary,
PAMI(41), No. 12, December 2019, pp. 2861-2874.
IEEE DOI 1911
BibRef
Earlier:
Low-Rank Embedded Ensemble Semantic Dictionary for Zero-Shot Learning,
CVPR17(6005-6013)
IEEE DOI 1711
BibRef
Earlier:
Deep Robust Encoder Through Locality Preserving Low-Rank Dictionary,
ECCV16(VI: 567-582).
Springer DOI 1611
Semantics, Visualization, Dictionaries, Generative adversarial networks, Training data, Data models, zero-shot learning. Gold, Information science, Machine learning, Semantics, Training, Visualization BibRef

Ding, Z.M.[Zheng-Ming], Shao, M.[Ming], Fu, Y.[Yun],
Missing Modality Transfer Learning via Latent Low-Rank Constraint,
IP(24), No. 11, November 2015, pp. 4322-4334.
IEEE DOI 1509
learning (artificial intelligence) BibRef

Long, M.S.[Ming-Sheng], Cao, Y.[Yue], Cao, Z.J.[Zhang-Jie], Wang, J.M.[Jian-Min], Jordan, M.I.[Michael I.],
Transferable Representation Learning with Deep Adaptation Networks,
PAMI(41), No. 12, December 2019, pp. 3071-3085.
IEEE DOI 1911
BibRef
Earlier: A3, A1, A4, A5, Only:
Partial Transfer Learning with Selective Adversarial Networks,
CVPR18(2724-2732)
IEEE DOI 1812
Task analysis, Learning systems, Adaptation models, Convolutional neural networks, Deep learning, Domain adaptation, multiple kernel learning. Feature extraction, Task analysis, Standards, Big Data, Bridges, Training, Labeling BibRef

Zhu, R.X.[Rui-Xi], Yan, L.[Li], Mo, N.[Nan], Liu, Y.[Yi],
Semi-supervised center-based discriminative adversarial learning for cross-domain scene-level land-cover classification of aerial images,
PandRS(155), 2019, pp. 72-89.
Elsevier DOI 1908
Semi-supervised domain adaptation, Scene-level land-cover classification, Triplet network, Center loss BibRef

Ma, X.H.[Xin-Hong], Zhang, T.Z.[Tian-Zhu], Xu, C.S.[Chang-Sheng],
Deep Multi-Modality Adversarial Networks for Unsupervised Domain Adaptation,
MultMed(21), No. 9, September 2019, pp. 2419-2431.
IEEE DOI 1909
BibRef
And:
GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation,
CVPR19(8258-8268).
IEEE DOI 2002
Feature extraction, Task analysis, Semantics, Training, Adaptation models, Correlation, Data mining, social event recognition BibRef

Ma, X.H.[Xin-Hong], Gao, J.Y.[Jun-Yu], Xu, C.S.[Chang-Sheng],
Active Universal Domain Adaptation,
ICCV21(8948-8957)
IEEE DOI 2203
Adaptation models, Uncertainty, Target recognition, Annotations, Computational modeling, Semantics, Representation learning BibRef

Zuo, Y.K.[Yu-Kun], Yao, H.T.[Han-Tao], Zhuang, L.S.[Lian-Sheng], Xu, C.S.[Chang-Sheng],
Dual Structural Knowledge Interaction for Domain Adaptation,
MultMed(25), 2023, pp. 9057-9070.
IEEE DOI 2312
BibRef

Zhou, Q.A.[Qi-Ang], Zhou, W.[Wen'an], Yang, B.[Bin], Huan, J.[Jun],
Deep cycle autoencoder for unsupervised domain adaptation with generative adversarial networks,
IET-CV(13), No. 7, Octomber 2019, pp. 659-665.
DOI Link 1911
BibRef

Shao, R.[Rui], Lan, X.Y.[Xiang-Yuan],
Adversarial auto-encoder for unsupervised deep domain adaptation,
IET-IPR(13), No. 14, 12 December 2019, pp. 2772-2777.
DOI Link 1912
BibRef

Yang, S.[Shu], Wang, Y.W.[Yao-Wei], Shi, Y.M.[Ye-Min], Fei, Z.S.[Ze-Song],
Can Categories and Attributes Be Learned in a Multi-Task Way?,
MultMed(21), No. 12, December 2019, pp. 3194-3204.
IEEE DOI 1912
Task analysis, Object recognition, Birds, Training, Dogs, Cats, Predictive models, Multi-task learning, regularization BibRef

Yang, S.[Shu], Wang, Y.W.[Yao-Wei], Chen, K.[Ke], Zeng, W.[Wei], Fei, Z.S.[Ze-Song],
Attribute-Aware Feature Encoding for Object Recognition and Segmentation,
MultMed(24), 2022, pp. 3611-3623.
IEEE DOI 2207
Correlation, Semantics, Training data, Benchmark testing, Multitasking, Feature extraction, Encoding, Object recognition, regularization BibRef

Chadha, A., Andreopoulos, Y.,
Improved Techniques for Adversarial Discriminative Domain Adaptation,
IP(29), 2020, pp. 2622-2637.
IEEE DOI 2001
Task analysis, Training, Sensors, Proposals, Cameras, Neuromorphics, Adversarial methods, domain adaptation, neuromorphic vision sensing BibRef

Gholami, B., Sahu, P., Rudovic, O., Bousmalis, K., Pavlovic, V.,
Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach,
IP(29), 2020, pp. 3993-4002.
IEEE DOI 2002
Domain adaptation, mutual information, variational inference, adversarial learning BibRef

Yan, L., Fan, B., Liu, H., Huo, C., Xiang, S., Pan, C.,
Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images,
GeoRS(58), No. 5, May 2020, pp. 3558-3573.
IEEE DOI 2005
Domain adaptation (DA), pixel-level classification, self-training, triplet adversarial learning, very high resolution (VHR) BibRef

Rahman, M.M.[Mohammad Mahfujur], Fookes, C.[Clinton], Baktashmotlagh, M.[Mahsa], Sridharan, S.[Sridha],
Correlation-aware adversarial domain adaptation and generalization,
PR(100), 2020, pp. 107124.
Elsevier DOI 2005
Domain adaptation, Domain generalization, Correlation-alignment, Adversarial learning BibRef

Bu, K., He, Y., Jing, X., Han, J.,
Adversarial Transfer Learning for Deep Learning Based Automatic Modulation Classification,
SPLetters(27), 2020, pp. 880-884.
IEEE DOI 2006
Adversarial transfer learning, domain adaptation, modulation recognition, sampling frequency BibRef

Hou, X.X.[Xian-Xu], Liu, J.X.[Jing-Xin], Xu, B.[Bolei], Wang, X.L.[Xiao-Long], Liu, B.Z.[Bo-Zhi], Qiu, G.P.[Guo-Ping],
Class-aware domain adaptation for improving adversarial robustness,
IVC(99), 2020, pp. 103926.
Elsevier DOI 2006
Domain adaptation, Adversarial robustness BibRef

Chen, W.D.[Wen-Dong], Hu, H.F.[Hai-Feng],
Generative attention adversarial classification network for unsupervised domain adaptation,
PR(107), 2020, pp. 107440.
Elsevier DOI 2008
Unsupervised domain adaptation, Generated adversarial network, Attention learning, Pseudo labels BibRef

Qiu, W.J.[Wen-Jie], Chen, W.D.[Wen-Dong], Hu, H.F.[Hai-Feng],
Partial domain adaptation based on shared class oriented adversarial network,
CVIU(199), 2020, pp. 103018.
Elsevier DOI 2009
Knowledge transfer, Partial domain adaptation, Adversarial network, Weighted class sampling BibRef

Yuan, Y.[Yumeng], Li, Y.H.[Yu-Hua], Zhu, Z.L.[Zhen-Long], Li, R.X.[Rui-Xuan], Gu, X.[Xiwu],
Adversarial joint domain adaptation of asymmetric feature mapping based on least squares distance,
PRL(136), 2020, pp. 251-256.
Elsevier DOI 2008
Joint domain adaptation, Adversarial learning, Asymmetric feature mapping, Conditional distribution alignment BibRef

Zhang, Y.[Yun], Wang, N.B.[Nian-Bin], Cai, S.B.[Shao-Bin],
Adversarial sliced Wasserstein domain adaptation networks,
IVC(102), 2020, pp. 103974.
Elsevier DOI 2010
Transfer learning, Domain adaptation, Image classification, Adversarial learning BibRef

Guan, D.[Dayan], Huang, J.X.[Jia-Xing], Lu, S.J.[Shi-Jian], Xiao, A.[Aoran],
Scale variance minimization for unsupervised domain adaptation in image segmentation,
PR(112), 2021, pp. 107764.
Elsevier DOI 2102
Unsupervised domain adaptation, Image segmentation, Semantic structure, Variance minimization, Adversarial learning BibRef

Ma, C.[Chenhui], Sha, D.[Dexuan], Mu, X.D.[Xiao-Dong],
Unsupervised Adversarial Domain Adaptation with Error-Correcting Boundaries and Feature Adaption Metric for Remote-Sensing Scene Classification,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Dundar, A.[Aysegul], Liu, M.Y.[Ming-Yu], Yu, Z.D.[Zhi-Ding], Wang, T.C.[Ting-Chun], Zedlewski, J.[John], Kautz, J.[Jan],
Domain Stylization: A Fast Covariance Matching Framework Towards Domain Adaptation,
PAMI(43), No. 7, July 2021, pp. 2360-2372.
IEEE DOI 2106
Image segmentation, Semantics, Training, Task analysis, Adaptation models, Data models, Domain adaptation, object detection BibRef

Zhang, W.C.[Wei-Chen], Xu, D.[Dong], Ouyang, W.L.[Wan-Li], Li, W.[Wen],
Self-Paced Collaborative and Adversarial Network for Unsupervised Domain Adaptation,
PAMI(43), No. 6, June 2021, pp. 2047-2061.
IEEE DOI 2106
BibRef
Earlier: A1, A3, A4, A2:
Collaborative and Adversarial Network for Unsupervised Domain Adaptation,
CVPR18(3801-3809)
IEEE DOI 1812
Task analysis, Streaming media, Training, Collaboration, Object recognition, Visualization, Optical imaging, self-paced learning. Training, Feature extraction, Adaptation models, Task analysis BibRef

Zhao, S.C.[Si-Cheng], Li, B.[Bo], Xu, P.F.[Peng-Fei], Yue, X.Y.[Xiang-Yu], Ding, G.G.[Gui-Guang], Keutzer, K.[Kurt],
MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation,
IJCV(129), No. 8, August 2021, pp. 2399-2424.
Springer DOI 2108
BibRef

Xu, L.[Li], Zhou, Y.D.[Yao-Dong], Luo, B.[Bing], Li, B.[Bo], Zhang, C.[Chao],
Adversarial domain adaptation with Siamese network for video object cosegmentation,
SP:IC(123), 2024, pp. 117109.
Elsevier DOI 2403
Video object cosegmentation, Domain adaption, Adversarial learning, Siamese network, Classifying network BibRef

Wang, Z.[Zi], Sun, X.L.[Xiao-Liang], Su, A.[Ang], Wang, G.[Gang], Li, Y.[Yang], Yu, Q.F.[Qi-Feng],
Improve conditional adversarial domain adaptation using self-training,
IET-IPR(15), No. 10, 2021, pp. 2169-2178.
DOI Link 2108
BibRef

Li, J.J.[Jing-Jing], Chen, E.[Erpeng], Ding, Z.M.[Zheng-Ming], Zhu, L.[Lei], Lu, K.[Ke], Shen, H.T.[Heng Tao],
Maximum Density Divergence for Domain Adaptation,
PAMI(43), No. 11, November 2021, pp. 3918-3930.
IEEE DOI 2110
Measurement, Training, Kernel, Task analysis, Adaptation models, Benchmark testing, Games, Domain adaptation, transfer learning, adversarial learning BibRef

Li, Y.Z.[Yue-Zun], Chang, M.C.[Ming-Ching], Sun, P.[Pu], Qi, H.G.[Hong-Gang], Dong, J.Y.[Jun-Yu], Lyu, S.W.[Si-Wei],
TransRPN: Towards the Transferable Adversarial Perturbations using Region Proposal Networks and Beyond,
CVIU(213), 2021, pp. 103302.
Elsevier DOI 2112
Transferable adversarial perturbation, Object detection BibRef

Hu, D.P.[Da-Peng], Liang, J.[Jian], Hou, Q.B.[Qi-Bin], Yan, H.[Hanshu], Chen, Y.P.[Yun-Peng],
Adversarial Domain Adaptation With Prototype-Based Normalized Output Conditioner,
IP(30), 2021, pp. 9359-9371.
IEEE DOI 2112
Training, Task analysis, Semantics, Sensitivity, Object recognition, Predictive models, Prototypes, Domain adaptation, pseudo-labels BibRef

Han, K.[Keji], Xia, B.[Bin], Li, Y.[Yun],
(AD)2: Adversarial domain adaptation to defense with adversarial perturbation removal,
PR(122), 2022, pp. 108303.
Elsevier DOI 2112
Deep learning, Adversarial example, Domain adaptation BibRef

Wu, Y.[Yuan], Inkpen, D.[Diana], El-Roby, A.[Ahmed],
Towards Category and Domain Alignment: Category-Invariant Feature Enhancement for Adversarial Domain Adaptation,
AROW21(132-141)
IEEE DOI 2112
Adaptation models, System performance, Measurement uncertainty, Benchmark testing, Distortion measurement BibRef

Duan, Y.X.[Ye-Xin], Zou, J.H.[Jun-Hua], Zhou, X.Y.[Xing-Yu], Zhang, W.[Wu], Zhang, J.[Jin], Pan, Z.S.[Zhi-Song],
Enhancing transferability of adversarial examples via rotation-invariant attacks,
IET-CV(16), No. 1, 2022, pp. 1-11.
DOI Link 2202
BibRef

Chen, J.W.[Jia-Wei], Zhang, Z.Q.[Zi-Qi], Xie, X.P.[Xin-Peng], Li, Y.X.[Yue-Xiang], Xu, T.[Tao], Ma, K.[Kai], Zheng, Y.F.[Ye-Feng],
Beyond Mutual Information: Generative Adversarial Network for Domain Adaptation Using Information Bottleneck Constraint,
MedImg(41), No. 3, March 2022, pp. 595-607.
IEEE DOI 2203
Generative adversarial networks, Task analysis, Image segmentation, Adaptation models, Training, domain adaptation BibRef

Wen, J.[Jun], Yuan, J.S.[Jun-Song], Zheng, Q.[Qian], Liu, R.S.[Ri-Sheng], Gong, Z.F.[Zhe-Feng], Zheng, N.G.[Neng-Gan],
Hierarchical domain adaptation with local feature patterns,
PR(124), 2022, pp. 108445.
Elsevier DOI 2203
Domain adaptation, Local feature patterns, Adversarial learning, Hierarchical alignment BibRef

Wang, B.[Biao], Zhu, L.X.[Ling-Xuan], Guo, X.[Xing], Wang, X.B.[Xia-Bing], Wu, J.J.[Jia-Ji],
SDTGAN: Generation Adversarial Network for Spectral Domain Translation of Remote Sensing Images of the Earth Background Based on Shared Latent Domain,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Tang, H.[Hui], Wang, Y.[Yaowei], Jia, K.[Kui],
Unsupervised domain adaptation via distilled discriminative clustering,
PR(127), 2022, pp. 108638.
Elsevier DOI 2205
Deep learning, Unsupervised domain adaptation, Image classification, Knowledge distillation, Implicit domain alignment BibRef

Tang, H.[Hui], Zhu, X.T.[Xia-Tian], Chen, K.[Ke], Jia, K.[Kui], Chen, C.L.P.[C. L. Philip],
Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation Using Structurally Regularized Deep Clustering,
PAMI(44), No. 10, October 2022, pp. 6517-6533.
IEEE DOI 2209
BibRef
Earlier: A1, A3, A4, Only:
Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering,
CVPR20(8722-8732)
IEEE DOI 2008
Semantics, Image segmentation, Task analysis, Benchmark testing, Training, Data models, Adaptation models, Domain adaptation, semantic segmentation. Benchmark testingn, Fasteners, Clustering methods, Feature extraction BibRef

Xie, J.W.[Jian-Wen], Zheng, Z.L.[Zi-Long], Fang, X.L.[Xiao-Lin], Zhu, S.C.[Song-Chun], Wu, Y.N.[Ying Nian],
Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning,
PAMI(44), No. 8, August 2022, pp. 3957-3973.
IEEE DOI 2207
Linear programming, Iterative methods, Generators, Training, Task analysis, Planning, Deep generative models, conditional learning BibRef

Li, S.[Shuang], Xie, B.H.[Bin-Hui], Lin, Q.X.[Qiu-Xia], Liu, C.H.[Chi Harold], Huang, G.[Gao], Wang, G.R.[Guo-Ren],
Generalized Domain Conditioned Adaptation Network,
PAMI(44), No. 8, August 2022, pp. 4093-4109.
IEEE DOI 2207
Task analysis, Feature extraction, Adaptation models, Training, Convolutional codes, Painting, Knowledge engineering, channel attention BibRef

Gao, L.L.[Lian-Li], Huang, Z.J.[Zi-Jie], Song, J.K.[Jing-Kuan], Yang, Y.[Yang], Shen, H.T.[Heng Tao],
Push & Pull: Transferable Adversarial Examples With Attentive Attack,
MultMed(24), 2022, pp. 2329-2338.
IEEE DOI 2205
Perturbation methods, Feature extraction, Computational modeling, Task analysis, Predictive models, Neural networks, targeted attack BibRef

Ji, S.[Sangwoo], Park, N.[Namgyu], Na, D.B.[Dong-Bin], Zhu, B.[Bin], Kim, J.[Jong],
Defending against attacks tailored to transfer learning via feature distancing,
CVIU(223), 2022, pp. 103533.
Elsevier DOI 2210
Robust transfer learning, Adversarial example, Triplet loss, Mimic attack, Target-agnostic attack BibRef

Li, J.J.[Jing-Jing], Du, Z.[Zhekai], Zhu, L.[Lei], Ding, Z.M.[Zheng-Ming], Lu, K.[Ke], Shen, H.T.[Heng Tao],
Divergence-Agnostic Unsupervised Domain Adaptation by Adversarial Attacks,
PAMI(44), No. 11, November 2022, pp. 8196-8211.
IEEE DOI 2210
Adaptation models, Training, Feature extraction, Measurement, Data models, Neural networks, Semantics, model adaptation BibRef

Zhu, Y.[Yao], Chen, Y.F.[Yue-Feng], Li, X.D.[Xiao-Dan], Chen, K.J.[Ke-Jiang], He, Y.[Yuan], Tian, X.[Xiang], Zheng, B.[Bolun], Chen, Y.W.[Yao-Wu], Huang, Q.M.[Qing-Ming],
Toward Understanding and Boosting Adversarial Transferability from a Distribution Perspective,
IP(31), 2022, pp. 6487-6501.
IEEE DOI 2211
Data models, Perturbation methods, Iterative methods, Training, Distributed databases, Predictive models, Neural networks, black-box attack BibRef

Xu, Y.C.[Yue-Cong], Yang, J.F.[Jian-Fei], Cao, H.Z.[Hao-Zhi], Wu, K.Y.[Ke-Yu], Wu, M.[Min], Li, Z.G.[Zheng-Guo], Chen, Z.H.[Zheng-Hua],
Multi-Source Video Domain Adaptation With Temporal Attentive Moment Alignment Network,
CirSysVideo(33), No. 8, August 2023, pp. 3860-3871.
IEEE DOI 2308
Task analysis, Benchmark testing, Feature extraction, Training, Measurement, Neural networks, Kinetic theory, Multi-source, dataset BibRef

Xu, Y.C.[Yue-Cong], Yang, J.F.[Jian-Fei], Cao, H.Z.[Hao-Zhi], Chen, Z.H.[Zheng-Hua], Li, Q.[Qi], Mao, K.Z.[Ke-Zhi],
Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network,
ICCV21(9312-9321)
IEEE DOI 2203
Codes, Handheld computers, Filtration, Filtering, Benchmark testing, Videos, Transfer/Low-shot/Semi/Unsupervised Learning, Video analysis and understanding BibRef

Wu, K.Y.[Ke-Yu], Wu, M.[Min], Chen, Z.H.[Zheng-Hua], Jin, R.B.[Rui-Bing], Cui, W.[Wei], Cao, Z.G.[Zhi-Guang], Li, X.L.[Xiao-Li],
Reinforced Adaptation Network for Partial Domain Adaptation,
CirSysVideo(33), No. 5, May 2023, pp. 2370-2380.
IEEE DOI 2305
Adaptation models, Reinforcement learning, Knowledge transfer, Training, Data models, Task analysis, Minimization, transfer learning BibRef

Zhe, X.[Xiao], Du, Z.[Zhekai], Lou, C.[Chunwei], Li, J.J.[Jing-Jing],
Alleviating the generalization issue in adversarial domain adaptation networks,
IVC(135), 2023, pp. 104695.
Elsevier DOI 2306
Domain adaptation, Transfer learning, Adversarial learning BibRef

Liu, D.Z.[Dai-Zong], Hu, W.[Wei],
Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification,
PAMI(45), No. 4, April 2023, pp. 4727-4746.
IEEE DOI 2303
Point cloud compression, Solid modeling, Perturbation methods, Data models, Distortion, Atmospheric modeling, defense on adversarial attacks BibRef

Xiang, W.Z.[Wen-Zhao], Su, H.[Hang], Liu, C.[Chang], Guo, Y.D.[Yan-Dong], Zheng, S.[Shibao],
Improving the robustness of adversarial attacks using an affine-invariant gradient estimator,
CVIU(229), 2023, pp. 103647.
Elsevier DOI 2303
Adversarial attack, Deep neural networks, Affine invariance, Transferability BibRef

Li, T.B.[Tian-Bao], Su, Y.T.[Yu-Ting], Song, D.[Dan], Li, W.H.[Wen-Hui], Wei, Z.Q.[Zhi-Qiang], Liu, A.A.[An-An],
Progressive Fourier Adversarial Domain Adaptation for Object Classification and Retrieval,
MultMed(26), 2024, pp. 4540-4553.
IEEE DOI 2403
Solid modeling, Task analysis, Measurement, Training, Adaptation models, Semantics, Domain adaptation, metric learning, cross-domain 3D model retrieval BibRef


Liu, X.N.[Xuan-Nan], Zhong, Y.Y.[Yao-Yao], Zhang, Y.H.[Yu-Hang], Qin, L.X.[Li-Xiong], Deng, W.H.[Wei-Hong],
Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation,
ICCV23(4412-4421)
IEEE DOI Code:
WWW Link. 2401
BibRef

Yeo, T.[Teresa], Kar, O.F.[Oguzhan Fatih], Sodagar, Z.[Zahra], Zamir, A.[Amir],
Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback,
ICCV23(4651-4664)
IEEE DOI 2401
BibRef

Jeon, S.[Seogkyu], Liu, B.[Bei], Lee, P.[Pilhyeon], Hong, K.[Kibeom], Fu, J.L.[Jian-Long], Byun, H.R.[Hye-Ran],
Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations,
ICCV23(7224-7233)
IEEE DOI 2401
BibRef

Sushko, V.[Vadim], Wang, R.[Ruyu], Gall, J.[Juergen],
Smoothness Similarity Regularization for Few-Shot GAN Adaptation,
ICCV23(7050-7059)
IEEE DOI 2401
BibRef

Zhu, H.[Hegui], Ren, Y.C.[Yu-Chen], Sui, X.Y.[Xiao-Yan], Yang, L.P.[Lian-Ping], Jiang, W.[Wuming],
Boosting Adversarial Transferability via Gradient Relevance Attack,
ICCV23(4718-4727)
IEEE DOI Code:
WWW Link. 2401
BibRef

Xu, Z.[Zhuoer], Gu, Z.X.[Zhang-Xuan], Zhang, J.P.[Jian-Ping], Cui, S.[Shiwen], Meng, C.[Changhua], Wang, W.Q.[Wei-Qiang],
Backpropagation Path Search On Adversarial Transferability,
ICCV23(4640-4650)
IEEE DOI 2401
BibRef

Wang, X.S.[Xiao-Sen], Zhang, Z.L.[Ze-Liang], Zhang, J.P.[Jian-Ping],
Structure Invariant Transformation for better Adversarial Transferability,
ICCV23(4584-4596)
IEEE DOI Code:
WWW Link. 2401
BibRef

Chen, B.[Bin], Yin, J.L.[Jia-Li], Chen, S.[Shukai], Chen, B.[Bohao], Liu, X.[Ximeng],
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability,
ICCV23(4466-4475)
IEEE DOI Code:
WWW Link. 2401
BibRef

Byun, J.[Junyoung], Kwon, M.J.[Myung-Joon], Cho, S.[Seungju], Kim, Y.[Yoonji], Kim, C.[Changick],
Introducing Competition to Boost the Transferability of Targeted Adversarial Examples Through Clean Feature Mixup,
CVPR23(24648-24657)
IEEE DOI 2309
BibRef

Liu, Y.[Yiran], Feng, X.[Xin], Wang, Y.L.[Yun-Long], Yang, W.[Wu], Ming, D.[Di],
TRM-UAP: Enhancing the Transferability of Data-Free Universal Adversarial Perturbation via Truncated Ratio Maximization,
ICCV23(4739-4748)
IEEE DOI Code:
WWW Link. 2401
BibRef

Pathak, A.[Arkanath], Dufour, N.[Nicholas],
Sequential Training of GANs Against GAN-Classifiers Reveals Correlated 'Knowledge Gaps' Present Among Independently Trained GAN Instances,
CVPR23(24460-24469)
IEEE DOI 2309
BibRef

Wang, Z.B.[Zhi-Bo], Yang, H.[Hongshan], Feng, Y.H.[Yun-He], Sun, P.[Peng], Guo, H.C.[Heng-Chang], Zhang, Z.F.[Zhi-Fei], Rent, K.[Kui],
Towards Transferable Targeted Adversarial Examples,
CVPR23(20534-20543)
IEEE DOI 2309
BibRef

Liu, Y.[Ye], Qiao, L.F.[Ling-Feng], Lu, C.C.[Chang-Chong], Yin, D.[Di], Lin, C.[Chen], Peng, H.Y.[Hao-Yuan], Ren, B.[Bo],
OSAN: A One-Stage Alignment Network to Unify Multimodal Alignment and Unsupervised Domain Adaptation,
CVPR23(3551-3560)
IEEE DOI 2309
BibRef

Jin, X.[Xin], He, T.Y.[Tian-Yu], Shen, X.[Xu], Wu, S.H.[Song-Hua], Liu, T.L.[Tong-Liang], Ye, J.W.[Jing-Wen], Wang, X.C.[Xin-Chao], Huang, J.Q.[Jian-Qiang], Chen, Z.B.[Zhi-Bo], Hua, X.S.[Xian-Sheng],
Unleashing the Potential of Adaptation Models via Go-getting Domain Labels,
OutDistri22(308-325).
Springer DOI 2304
adversarial domain adaptation. BibRef

Pimpalkhute, V.[Varad], Kunde, S.[Shruti], Singhal, R.[Rekha],
GEMS: Generating Efficient Meta-Subnets,
WACV23(5304-5312)
IEEE DOI 2302
Training, Adaptation models, Image segmentation, Computational modeling, Reinforcement learning, Object detection, Vision + language and/or other modalities BibRef

Westfechtel, T.[Thomas], Yeh, H.W.[Hao-Wei], Meng, Q.[Qier], Mukuta, Y.[Yusuke], Harada, T.[Tatsuya],
Backprop Induced Feature Weighting for Adversarial Domain Adaptation with Iterative Label Distribution Alignment,
WACV23(392-401)
IEEE DOI 2302
Training, Deep learning, Limiting, Neural networks, Training data, Benchmark testing, Algorithms: Machine learning architectures, algorithms (including transfer) BibRef

Ye, Y.[Yalan], Wang, C.J.[Chun-Ji], Dong, H.[Hai], Lu, L.[Li], Zhao, Q.[Qiang],
Cross-session Specific Emitter Identification using Adversarial Domain Adaptation with Wasserstein distance,
ICPR22(3119-3124)
IEEE DOI 2212
Degradation, Adaptation models, Modulation, Receivers, Bandwidth, Fingerprint recognition, Data models BibRef

Huang, H.[Hao], Chen, C.[Cheng], Fang, Y.[Yi],
Manifold Adversarial Learning for Cross-Domain 3D Shape Representation,
ECCV22(XXVI:272-289).
Springer DOI 2211
BibRef

Webster, R.[Ryan], Rabin, J.[Julien], Simon, L.[Loïc], Jurie, F.[Frédéric],
Width-Wise Parameter Sharing for Multi-Domain GAN Learning,
ICIP22(4163-4167)
IEEE DOI 2211
Training, Image quality, Visualization, Adaptation models, Image coding, Transfer learning, Generators, Image generation, GANs, Network compression BibRef

Laria, H.[Héctor], Wang, Y.X.[Ya-Xing], van de Weijer, J.[Joost], Raducanu, B.[Bogdan],
Transferring Unconditional to Conditional GANs with Hyper-Modulation,
CLVision22(3839-3848)
IEEE DOI 2210
Training, Transfer learning, Modulation, Process control, Generative adversarial networks, Data models BibRef

Jang, D.G.[Dong-Gon], Son, S.[Sanghyeok], Kim, D.S.[Dae-Shik],
Strengthening the Transferability of Adversarial Examples Using Advanced Looking Ahead and Self-CutMix,
ArtOfRobust22(147-154)
IEEE DOI 2210
Training, Deep learning, Computational modeling, Perturbation methods, Neural networks BibRef

Wang, J.H.[Jing-Hao], Cui, C.L.[Chen-Ling], Wen, X.J.[Xue-Jun], Shi, J.[Jie],
Transpatch: A Transformer-based Generator for Accelerating Transferable Patch Generation in Adversarial Attacks Against Object Detection Models,
AdvRob22(317-331).
Springer DOI 2304
BibRef

Yang, X.[Xiao], Dong, Y.P.[Yin-Peng], Pang, T.Y.[Tian-Yu], Su, H.[Hang], Zhu, J.[Jun],
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks,
ECCV22(IV:725-742).
Springer DOI 2211
BibRef

Cai, Z.[Zikui], Rane, S.[Shantanu], Brito, A.E.[Alejandro E.], Song, C.Y.[Cheng-Yu], Krishnamurthy, S.V.[Srikanth V.], Roy-Chowdhury, A.K.[Amit K.], Asif, M.S.[M. Salman],
Zero-Query Transfer Attacks on Context-Aware Object Detectors,
CVPR22(15004-15014)
IEEE DOI 2210
Deep learning, Technological innovation, Image analysis, Perturbation methods, Neural networks, Detectors, Scene analysis and understanding BibRef

Li, J.T.[Jing-Tao], Rakin, A.S.[Adnan Siraj], Chen, X.[Xing], He, Z.Z.[Zhe-Zhi], Fan, D.L.[De-Liang], Chakrabarti, C.[Chaitali],
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning,
CVPR22(10184-10192)
IEEE DOI 2210
Training, Resistance, Federated learning, Computational modeling, Feature extraction, Pattern recognition, Servers, privacy and ethics in vision BibRef

Benz, P.[Philipp], Zhang, C.N.[Chao-Ning], Kweon, I.S.[In So],
Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective,
ICCV21(7798-7807)
IEEE DOI 2203
Radio frequency, Training, Integrated circuits, Deep learning, Costs, Neural networks, Adversarial learning, Explainable AI BibRef

Jin, X.[Xin], Lan, C.L.[Cui-Ling], Zeng, W.J.[Wen-Jun], Chen, Z.B.[Zhi-Bo],
Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptation,
ICCV21(9154-9163)
IEEE DOI 2203
Training, Drives, Benchmark testing, Feature extraction, Optimization, Transfer/Low-shot/Semi/Unsupervised Learning, Optimization and learning methods BibRef

Xia, H.F.[Hai-Feng], Zhao, H.D.[Han-Dong], Ding, Z.M.[Zheng-Ming],
Adaptive Adversarial Network for Source-free Domain Adaptation,
ICCV21(8990-8999)
IEEE DOI 2203
Training, Adaptation models, Data privacy, Adaptive systems, Target recognition, Semantics, Benchmark testing, BibRef

Gao, Z.Q.[Zhi-Qiang], Zhang, S.F.[Shu-Fei], Huang, K.[Kaizhu], Wang, Q.F.[Qiu-Feng], Zhong, C.L.[Chao-Liang],
Gradient Distribution Alignment Certificates Better Adversarial Domain Adaptation,
ICCV21(8917-8926)
IEEE DOI 2203
Adaptation models, Upper bound, Benchmark testing, Adversarial machine learning, Task analysis, Recognition and classification BibRef

Rangwani, H.[Harsh], Jain, A.[Arihant], Aithal, S.K.[Sumukh K], Babu, R.V.[R. Venkatesh],
S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation,
ICCV21(7496-7505)
IEEE DOI 2203
Uncertainty, Optimization, Adversarial learning, Recognition and classification, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Liu, X.F.[Xiao-Feng], Guo, Z.H.[Zhen-Hua], Li, S.[Site], Xing, F.X.[Fang-Xu], You, J.[Jane], Kuo, C.C.J.[C.C. Jay], El Fakhri, G.[Georges], Woo, J.H.[Jong-Hye],
Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate,
ICCV21(10347-10356)
IEEE DOI 2203
Training, Benchmark testing, Feature extraction, Adversarial machine learning, Task analysis, Optimization, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Mangla, P.[Puneet], Kumari, N.[Nupur], Singh, M.[Mayank], Krishnamurthy, B.[Balaji], Balasubramanian, V.N.[Vineeth N.],
Data InStance Prior (DISP) in Generative Adversarial Networks,
WACV22(3471-3481)
IEEE DOI 2202
Training, Image quality, Image synthesis, Transfer learning, Training data, Semi- and Un- supervised Learning BibRef

Tian, H.T.[Hai-Tao], Qu, S.[Shiru], Payeur, P.[Pierre],
Unsupervised Pixel-Wise Weighted Adversarial Domain Adaptation,
ISVC21(I:586-600).
Springer DOI 2112
BibRef

Inkawhich, N.[Nathan], Liang, K.J.[Kevin J.], Zhang, J.Y.[Jing-Yang], Yang, H.R.[Huan-Rui], Li, H.[Hai], Chen, Y.[Yiran],
Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?,
AROW21(41-50)
IEEE DOI 2112
Training, Sensitivity, Computational modeling, Predictive models, Boosting BibRef

Shahbazi, M.[Mohamad], Huang, Z.W.[Zhi-Wu], Paudel, D.P.[Danda Pani], Chhatkuli, A.[Ajad], Van Gool, L.J.[Luc J.],
Efficient Conditional GAN Transfer with Knowledge Propagation across Classes,
CVPR21(12162-12171)
IEEE DOI 2111
Training, Codes, Image synthesis, Generative adversarial networks, Pattern recognition, Task analysis BibRef

Elliott, A.[Andrew], Law, S.[Stephen], Russell, C.[Chris],
Explaining Classifiers using Adversarial Perturbations on the Perceptual Ball,
CVPR21(10688-10697)
IEEE DOI 2111
Location awareness, Bridges, Perturbation methods, Neural networks, Games, Benchmark testing BibRef

Phan, B.[Buu], Mannan, F.[Fahim], Heide, F.[Felix],
Adversarial Imaging Pipelines,
CVPR21(16046-16056)
IEEE DOI 2111
Stimulated emission, Image processing, Pipelines, Physical optics, Transforms, Cameras, Hardware BibRef

Chin, T.W.[Ting-Wu], Zhang, C.[Cha], Marculescu, D.[Diana],
Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness,
TCV21(3237-3246)
IEEE DOI 2109
Computational modeling, Transfer learning, Robustness, Pattern recognition, Noise measurement BibRef

Zunino, A.[Andrea], Bargal, S.A.[Sarah Adel], Volpi, R.[Riccardo], Sameki, M.[Mehrnoosh], Zhang, J.M.[Jian-Ming], Sclaroff, S.[Stan], Murino, V.[Vittorio], Saenko, K.[Kate],
Explainable Deep Classification Models for Domain Generalization,
TCV21(3227-3236)
IEEE DOI 2109
Training, Degradation, Measurement, Visualization, Computational modeling BibRef

Ustun, B.[Berkcan], Kaya, A.K.[Ahmet Kagan], Ayerden, E.C.[Ezgi Cakir], Altinel, F.[Fazil],
Spectral Transfer Guided Active Domain Adaptation For Thermal Imagery,
PBVS23(449-458)
IEEE DOI 2309
BibRef

Akkaya, I.B.[Ibrahim Batuhan], Altinel, F.[Fazil], Halici, U.[Ugur],
Self-training Guided Adversarial Domain Adaptation For Thermal Imagery,
PBVS21(4317-4326)
IEEE DOI 2109
Adaptation models, Lighting, Cameras, Pattern recognition BibRef

Guo, H.[Hao], Dolhansky, B.[Brian], Hsin, E.[Eric], Dinh, P.[Phong], Ferrer, C.C.[Cristian Canton], Wang, S.[Song],
Deep Poisoning: Towards Robust Image Data Sharing against Visual Disclosure,
WACV21(686-696)
IEEE DOI 2106
Training, Visualization, Toxicology, Training data, Image representation BibRef

Wang, T.X.[Tong-Xin], Ding, Z.M.[Zheng-Ming], Shao, W.[Wei], Tang, H.X.[Hai-Xu], Huang, K.[Kun],
Towards Fair Cross-Domain Adaptation via Generative Learning,
WACV21(454-463)
IEEE DOI 2106
Training, Visualization, Annotations, Training data, Data collection BibRef

Chavhan, R.[Ruchika], Jha, A.[Ankit], Banerjee, B.[Biplab], Chaudhuri, S.[Subhasis],
ADA-AT/DT: An Adversarial Approach for Cross-Domain and Cross-Task Knowledge Transfer,
WACV21(3501-3510)
IEEE DOI 2106
Training, Visualization, Correlation, Computational modeling, Semantics, Estimation, Robustness BibRef

Hu, J.[Jian], Tuo, H.Y.[Hong-Ya], Wang, C.[Chao], Qiao, L.F.[Ling-Feng], Zhong, H.W.[Hao-Wen], Yan, J.C.[Jun-Chi], Jing, Z.L.[Zhong-Liang], Leung, H.[Henry],
Discriminative Partial Domain Adversarial Network,
ECCV20(XXVII:632-648).
Springer DOI 2011
BibRef

Xia, H., Ding, Z.,
Structure Preserving Generative Cross-Domain Learning,
CVPR20(4363-4372)
IEEE DOI 2008
Feature extraction, Training, Measurement, Robustness, Adaptation models, Neural networks, Task analysis BibRef

Chen, E.C.[Erh-Chung], Lee, C.R.[Che-Rung],
Towards Fast and Robust Adversarial Training for Image Classification,
ACCV20(III:576-591).
Springer DOI 2103
BibRef

Raab, C.[Christoph], Väth, P.[Philipp], Meier, P.[Peter], Schleif, F.M.[Frank-Michael],
Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks,
ACCV20(III:457-473).
Springer DOI 2103
BibRef

Yang, J.F.[Jian-Fei], Zou, H.[Han], Zhou, Y.X.[Yu-Xun], Zeng, Z.Y.[Zhao-Yang], Xie, L.H.[Li-Hua],
Mind the Discriminability: Asymmetric Adversarial Domain Adaptation,
ECCV20(XXIV:589-606).
Springer DOI 2012
BibRef

Xia, H.F.[Hai-Feng], Ding, Z.M.[Zheng-Ming],
HGNet: Hybrid Generative Network for Zero-shot Domain Adaptation,
ECCV20(XXVII:55-70).
Springer DOI 2011
BibRef

Siry, R., Simon, L., Jurie, F.,
A Study Of Alignment Mechanisms In Adversarial Domain Adaptation,
ICIP20(1816-1820)
IEEE DOI 2011
Feature extraction, Training, Task analysis, Adaptation models, Algebra, Standards, Upper bound, Domain adaptation, Transfer learning BibRef

Xie, X.P.[Xin-Peng], Chen, J.W.[Jia-Wei], Li, Y.X.[Yue-Xiang], Shen, L.L.[Lin-Lin], Ma, K.[Kai], Zheng, Y.F.[Ye-Feng],
Self-Supervised CycleGAN for Object-preserving Image-to-Image Domain Adaptation,
ECCV20(XX:498-513).
Springer DOI 2011
BibRef

Zhou, K.Y.[Kai-Yang], Yang, Y.X.[Yong-Xin], Hospedales, T.M.[Timothy M.], Xiang, T.[Tao],
Learning to Generate Novel Domains for Domain Generalization,
ECCV20(XVI: 561-578).
Springer DOI 2010
BibRef

Wu, Y.[Yuan], Inkpen, D.[Diana], El-Roby, A.[Ahmed],
Dual Mixup Regularized Learning for Adversarial Domain Adaptation,
ECCV20(XXIX: 540-555).
Springer DOI 2010
BibRef

Yin, H., Molchanov, P., Alvarez, J.M., Li, Z., Mallya, A., Hoiem, D., Jha, N.K., Kautz, J.,
Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion,
CVPR20(8712-8721)
IEEE DOI 2008
Training, Adaptation models, Knowledge transfer, Neural networks, Task analysis, Training data, Image generation BibRef

Wang, S.N.[Si-Nan], Chen, X.Y.[Xin-Yang], Wang, Y.B.[Yun-Bo], Long, M.S.[Ming-Sheng], Wang, J.M.[Jian-Min],
Progressive Adversarial Networks for Fine-Grained Domain Adaptation,
CVPR20(9210-9219)
IEEE DOI 2008
Feature extraction, Adaptation models, Visualization, Task analysis, Birds, Training, Benchmark testing BibRef

Wang, Y., Gonzalez-Garcia, A., Berga, D., Herranz, L., Khan, F.S., van de Weijer, J.,
MineGAN: Effective Knowledge Transfer From GANs to Target Domains With Few Images,
CVPR20(9329-9338)
IEEE DOI 2008
Generators, Generative adversarial networks, Training, Data mining, Knowledge transfer, Computational modeling BibRef

Li, R., Jiao, Q., Cao, W., Wong, H., Wu, S.,
Model Adaptation: Unsupervised Domain Adaptation Without Source Data,
CVPR20(9638-9647)
IEEE DOI 2008
Adaptation models, Data models, Predictive models, Training, Generative adversarial networks, Generators BibRef

Wu, W., Su, Y., Chen, X., Zhao, S., King, I., Lyu, M.R., Tai, Y.,
Boosting the Transferability of Adversarial Samples via Attention,
CVPR20(1158-1167)
IEEE DOI 2008
Feature extraction, Training, Cats, Perturbation methods, Optimization, Predictive models, Computational modeling BibRef

Lu, Y., Jia, Y., Wang, J., Li, B., Chai, W., Carin, L., Velipasalar, S.,
Enhancing Cross-Task Black-Box Transferability of Adversarial Examples With Dispersion Reduction,
CVPR20(937-946)
IEEE DOI 2008
Task analysis, Dispersion, Feature extraction, Computational modeling, Machine learning, Image segmentation BibRef

Vivek, B.S., Babu, R.V.[R. Venkatesh],
Single-Step Adversarial Training With Dropout Scheduling,
CVPR20(947-956)
IEEE DOI 2008
Training, Robustness, Computational modeling, Perturbation methods, Machine learning, Iterative methods, Market research BibRef

Cui, S., Wang, S., Zhuo, J., Su, C., Huang, Q., Tian, Q.,
Gradually Vanishing Bridge for Adversarial Domain Adaptation,
CVPR20(12452-12461)
IEEE DOI 2008
Bridges, Generators, Training, Image reconstruction, Adaptation models, Games BibRef

Khare, V., Mahajan, D., Bharadhwaj, H., Verma, V.K., Rai, P.,
A Generative Framework for Zero-Shot Learning with Adversarial Domain Adaptation,
WACV20(3090-3099)
IEEE DOI 2006
Adaptation models, Training, Data models, Neural networks, Estimation, Covariance matrices, Training data BibRef

Morerio, P., Volpi, R., Ragonesi, R., Murino, V.,
Generative Pseudo-label Refinement for Unsupervised Domain Adaptation,
WACV20(3119-3128)
IEEE DOI 2006
Training, Robustness, Noise measurement, Generative adversarial networks, Adaptation models, Benchmark testing BibRef

Wang, Y.M.[Yi-Mu], Song, R.J.[Ren-Jie], Wei, X.S.[Xiu-Shen], Zhang, L.J.[Li-Jun],
An Adversarial Domain Adaptation Network For Cross-Domain Fine-Grained Recognition,
WACV20(1217-1225)
IEEE DOI 2006
Feature extraction, Task analysis, Image recognition, Adaptation models, Training, Measurement, Target recognition BibRef

Zheng, H., Zhang, Z., Gu, J., Lee, H., Prakash, A.,
Efficient Adversarial Training With Transferable Adversarial Examples,
CVPR20(1178-1187)
IEEE DOI 2008
Training, Perturbation methods, Robustness, Computational modeling, Measurement, Iterative methods, Silicon BibRef

Chen, P.P.[Pei-Peng], Gao, Y.[Yuan], Ma, A.J.[Andy J.],
Multi-level Attentive Adversarial Learning with Temporal Dilation for Unsupervised Video Domain Adaptation,
WACV22(776-785)
IEEE DOI 2202
Benchmark testing, Adversarial machine learning, Computational efficiency, Semi- and Un- supervised Learning BibRef

Pan, Y.S.[Young-Sun], Ma, A.J.[Andy J.], Gao, Y.[Yuan], Wang, J.P.[Jin-Peng], Lin, Y.Q.[Yi-Qi],
Multi-Scale Adversarial Cross-Domain Detection with Robust Discriminative Learning,
WACV20(1313-1321)
IEEE DOI 2006
Feature extraction, Object detection, Adaptation models, Robustness, Task analysis, Convolution, Training BibRef

Su, J., Tsai, Y., Sohn, K., Liu, B., Maji, S., Chandraker, M.,
Active Adversarial Domain Adaptation,
WACV20(728-737)
IEEE DOI 2006
Adaptation models, Uncertainty, Task analysis, Training, Data models, Object detection, Entropy BibRef

Rakin, A.S., He, Z., Fan, D.,
TBT: Targeted Neural Network Attack With Bit Trojan,
CVPR20(13195-13204)
IEEE DOI 2008
BibRef
Earlier:
Bit-Flip Attack: Crushing Neural Network With Progressive Bit Search,
ICCV19(1211-1220)
IEEE DOI 2004
Code, Neural Networks.
WWW Link. Trojan horses, Training, Computational modeling, Neurons, Training data, Quantization (signal), Neural networks. gradient methods, neural nets, security of data, Bit-flip attack, Deep Neural Network, DNN weight attack methodology, Degradation BibRef

Luo, Y.W.[Ya-Wei], Zheng, L.[Liang], Guan, T.[Tao], Yu, J.Q.[Jun-Qing], Yang, Y.[Yi],
Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation,
CVPR19(2502-2511).
IEEE DOI 2002
BibRef

Kornblith, S.[Simon], Shlens, J.[Jonathon], Le, Q.V.[Quoc V.],
Do Better ImageNet Models Transfer Better?,
CVPR19(2656-2666).
IEEE DOI 2002
BibRef

Xie, C.[Cihang], Zhang, Z.S.[Zhi-Shuai], Zhou, Y.Y.[Yu-Yin], Bai, S.[Song], Wang, J.Y.[Jian-Yu], Ren, Z.[Zhou], Yuille, A.L.[Alan L.],
Improving Transferability of Adversarial Examples With Input Diversity,
CVPR19(2725-2734).
IEEE DOI 2002
BibRef

Chen, Z.L.[Zi-Liang], Zhuang, J.Y.[Jing-Yu], Liang, X.D.[Xiao-Dan], Lin, L.[Liang],
Blending-Target Domain Adaptation by Adversarial Meta-Adaptation Networks,
CVPR19(2243-2252).
IEEE DOI 2002
BibRef

Agresti, G.[Gianluca], Schaefer, H.[Henrik], Sartor, P.[Piergiorgio], Zanuttigh, P.[Pietro],
Unsupervised Domain Adaptation for ToF Data Denoising With Adversarial Learning,
CVPR19(5579-5586).
IEEE DOI 2002
BibRef

Zhang, Y.B.[Ya-Bin], Tang, H.[Hui], Jia, K.[Kui], Tan, M.K.[Ming-Kui],
Domain-Symmetric Networks for Adversarial Domain Adaptation,
CVPR19(5026-5035).
IEEE DOI 2002
BibRef

Zhong, H., Tuo, H., Wang, C., Ren, X., Hu, J., Qiao, L.,
Source-Constraint Adversarial Domain Adaptation,
ICIP19(2486-2490)
IEEE DOI 1910
transfer learning, domain adaptation, adversarial network, metric learning BibRef

Kim, D., Lee, S., Kim, N., Jeong, S.,
Delegated Adversarial Training for Unsupervised Domain Adaptation,
ICIP19(2521-2525)
IEEE DOI 1910
Unsupervised domain adaptation, adversarial training, transfer learning BibRef

Romijnders, R.[Rob], Meletis, P., Dubbelman, G.,
A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation,
WACV19(1866-1875)
IEEE DOI 1904
image segmentation, semantic networks, unsupervised learning, unsupervised adversarial domain adaptation, Biological neural networks BibRef

Fu, H.[Huan], Gong, M.M.[Ming-Ming], Wang, C.[Chaohui], Batmanghelich, K.[Kayhan], Zhang, K.[Kun], Tao, D.C.[Da-Cheng],
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping,
CVPR19(2422-2431).
IEEE DOI 2002
BibRef

Ge, H.W.[Hong-Wei], Yao, Y.[Yao], Chen, Z.[Zheng], Sun, L.[Liang],
Unsupervised Transformation Network Based on GANs for Target-Domain Oriented Multi-Domain Image Translation,
ACCV18(II:398-413).
Springer DOI 1906
BibRef

Anoosheh, A., Agustsson, E., Timofte, R., Van Gool, L.J.,
ComboGAN: Unrestrained Scalability for Image Domain Translation,
Restoration18(896-8967)
IEEE DOI 1812
Training, Generators, Decoding, Task analysis, Data models BibRef

Zhang, J., Ding, Z., Li, W., Ogunbona, P.,
Importance Weighted Adversarial Nets for Partial Domain Adaptation,
CVPR18(8156-8164)
IEEE DOI 1812
Feature extraction, Task analysis, Training, Games, Neural networks BibRef

Li, H., Pan, S.J., Wang, S., Kot, A.C.,
Domain Generalization with Adversarial Feature Learning,
CVPR18(5400-5409)
IEEE DOI 1812
Data models, Training, Training data, Adaptation models, Decoding, Predictive models BibRef

Li, R., Cao, W., Qian, S., Wong, H., Wu, S.,
Cross-domain Semantic Feature Learning via Adversarial Adaptation Networks,
ICPR18(37-42)
IEEE DOI 1812
Feature extraction, Semantics, Task analysis, Adaptation models, Data mining, Computational modeling, Generators, adversarial learning BibRef

Hong, W.X.[Wei-Xiang], Wang, Z.Z.[Zhen-Zhen], Yang, M.[Ming], Yuan, J.S.[Jun-Song],
Conditional Generative Adversarial Network for Structured Domain Adaptation,
CVPR18(1335-1344)
IEEE DOI 1812
Semantics, Image segmentation, Generators, Training, Adaptation models, Neural networks, BibRef

Chen, Q.C.[Qing-Chao], Liu, Y.[Yang], Wang, Z.W.[Zhao-Wen], Wassell, I.[Ian], Chetty, K.[Kevin],
Re-Weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation,
CVPR18(7976-7985)
IEEE DOI 1812
Feature extraction, Training, Task analysis, Adaptation models, Neural networks, Loss measurement BibRef

Sankaranarayanan, S., Balaji, Y., Castillo, C.D., Chellappa, R.,
Generate to Adapt: Aligning Domains Using Generative Adversarial Networks,
CVPR18(8503-8512)
IEEE DOI 1812
Generators, Training, Adaptation models, Image generation, Data models, Task analysis BibRef

Fang, Y., Yuan, Q., Zhang, W., Zhang, Z.,
Diversified Dual Domain-Adversarial Neural Networks,
ICPR18(615-620)
IEEE DOI 1812
Feature extraction, Adaptation models, Training, Task analysis, Neural networks, Data models BibRef

Kang, G.L.[Guo-Liang], Zheng, L.[Liang], Yan, Y.[Yan], Yang, Y.[Yi],
Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: The Benefit of Target Expectation Maximization,
ECCV18(XI: 420-436).
Springer DOI 1810
BibRef

Li, Y.[Ya], Tian, X.[Xinmei], Gong, M.M.[Ming-Ming], Liu, Y.J.[Ya-Jing], Liu, T.L.[Tong-Liang], Zhang, K.[Kun], Tao, D.C.[Da-Cheng],
Deep Domain Generalization via Conditional Invariant Adversarial Networks,
ECCV18(XV: 647-663).
Springer DOI 1810
BibRef

Cao, Z.J.[Zhang-Jie], Ma, L.J.[Li-Jia], Long, M.S.[Ming-Sheng], Wang, J.M.[Jian-Min],
Partial Adversarial Domain Adaptation,
ECCV18(VIII: 139-155).
Springer DOI 1810
BibRef

Yan, L., Fan, B., Xiang, S., Pan, C.,
Adversarial Domain Adaptation with a Domain Similarity Discriminator for Semantic Segmentation of Urban Areas,
ICIP18(1583-1587)
IEEE DOI 1809
Urban areas, Semantics, Feature extraction, Image segmentation, Task analysis, Training, Labeling, domain adaptation, domain shift, urban areas BibRef

Liu, Y., Wang, Z., Jin, H., Wassell, I.,
Multi-task Adversarial Network for Disentangled Feature Learning,
CVPR18(3743-3751)
IEEE DOI 1812
Training, Generators, Task analysis, Feature extraction, Image generation, Optimization BibRef

Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.,
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks,
CVPR17(95-104)
IEEE DOI 1711
Adaptation models, Feature extraction, Generators, Training BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Few Shot Learning .


Last update:Apr 10, 2024 at 09:54:40