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, Gallium nitride, 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
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
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
Rakshit, S.[Sayan],
Tamboli, D.[Dipesh],
Meshram, P.S.[Pragati Shuddhodhan],
Banerjee, B.[Biplab],
Roig, G.[Gemma],
Chaudhuri, S.[Subhasis],
Multi-source Open-set Deep Adversarial Domain Adaptation,
ECCV20(XXVI:735-750).
Springer DOI
2011
BibRef
Rakshit, S.[Sayan],
Banerjee, B.[Biplab],
Roig, G.[Gemma],
Chaudhuri, S.[Subhasis],
Unsupervised Multi-source Domain Adaptation Driven by Deep Adversarial
Ensemble Learning,
GCPR19(485-498).
Springer DOI
1911
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
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
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Zero-Shot Learning .