14.1.4.4.2 Adversarial Networks for Transfer Learning, Domain Adaption

Chapter Contents (Back)
Transfer Learning. Domain Adaptation. Adversarial Networks. See also Data Hiding, Steganography, Adversarial Networks, Convolutional Networks. GAN

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.[Xiangyuan],
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


Rakin, A.S., He, Z., Fan, D.,
Bit-Flip Attack: Crushing Neural Network With Progressive Bit Search,
ICCV19(1211-1220)
IEEE DOI 2004
Code, Neural Networks.
WWW Link. 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.[Zhishuai], 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

Zhang, Y.P.[Ya-Ping], Nie, S.[Shuai], Liu, W.[Wenju], Xu, X.[Xing], Zhang, D.X.[Dong-Xiang], Shen, H.T.[Heng Tao],
Sequence-To-Sequence Domain Adaptation Network for Robust Text Image Recognition,
CVPR19(2735-2744).
IEEE DOI 2002
BibRef

Chen, Z.[Ziliang], 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.[Yabin], Tang, H.[Hui], Jia, K.[Kui], Tan, M.[Mingkui],
Domain-Symmetric Networks for Adversarial Domain Adaptation,
CVPR19(5026-5035).
IEEE DOI 2002
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 .


Last update:May 26, 2020 at 12:00:59