14.1.6.3 Adversarial Networks for Transfer Learning, Domain Adaption

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
computer vision, learning (artificial intelligence), neural nets, common knowledge, computer vision field, 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.[Xinhong], 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

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

Koo, S.[Sangjun], Yu, H.[Hwanjo], Lee, G.G.[Gary Geunbae],
Adversarial approach to domain adaptation for reinforcement learning on dialog systems,
PRL(128), 2019, pp. 467-473.
Elsevier DOI 1912
Dialog systems, Reinforcement learning, Domain adaptation, Transfer learning, Deep Q Network, Adversarial networks 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., Wang, Y., Shi, Y., Fei, Z.,
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

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.[Xianxu], Liu, J.X.[Jing-Xin], Xu, B.[Bolei], Wang, X.L.[Xiao-Long], Liu, B.[Bozhi], 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.[Shijian], 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

Wang, Z.[Zi], Sun, X.L.[Xiao-Liang], Su, A.[Ang], Wang, G.[Gang], Li, Y.[Yang], Yu, Q.[Qifeng],
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

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

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.[Yuxun], 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

Zhang, J.O.[Jeffrey O.], Sax, A.[Alexander], Zamir, A.[Amir], Guibas, L.J.[Leonidas J.], Malik, J.[Jitendra],
Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks,
ECCV20(III:698-714).
Springer DOI 2012
Adapt pre-trained network, not start from beginning. 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

Tang, H., Chen, K., Jia, K.,
Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering,
CVPR20(8722-8732)
IEEE DOI 2008
Task analysis, Training, Benchmark testing, Silicon carbide, Fasteners, Clustering methods, Feature extraction 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, Computer vision 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

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

Guo, Y.[Yunhui], Shi, H.H.[Hong-Hui], Kumar, A.[Abhishek], Grauman, K.[Kristen], Rosing, T.[Tajana], Feris, R.[Rogerio],
SpotTune: Transfer Learning Through Adaptive Fine-Tuning,
CVPR19(4800-4809).
IEEE DOI 2002
BibRef

Xie, C.[Cihang], Zhang, Z.S.[Zhi-Shuai], Zhou, Y.[Yuyin], 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, Computer vision, 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, Computer vision, 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
Zero-Shot Learning .