14.1.6.3 Domain Generalization

Chapter Contents (Back)
Domain Adaption. Domain Generalization.

Li, W.[Wen], Xu, Z.[Zheng], Xu, D.[Dong], Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Domain Generalization and Adaptation Using Low Rank Exemplar SVMs,
PAMI(40), No. 5, May 2018, pp. 1114-1127.
IEEE DOI 1804
Linear programming, Logistics, Support vector machines, Testing, Training, Videos, Visualization, Latent domains, domain adaptation, exemplar SVMs BibRef

Wang, Y., Li, W., Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Deep Domain Adaptation by Geodesic Distance Minimization,
TASKCV17(2651-2657)
IEEE DOI 1802
Adaptation models, Covariance matrices, Euclidean distance, Feature extraction, Manifolds, Training data, Visualization BibRef

Lee, W.[Woojin], Kim, H.[Hoki], Lee, J.W.[Jae-Wook],
Compact class-conditional domain invariant learning for multi-class domain adaptation,
PR(112), 2021, pp. 107763.
Elsevier DOI 2102
Domain adaptation, Generalization bound, Class-conditional domain invariant learning, Transfer Learning BibRef

Li, H.L.[Hao-Liang], Wang, S.Q.[Shi-Qi], Wan, R.J.[Ren-Jie], Kot, A.C.[Alex C.],
GMFAD: Towards Generalized Visual Recognition via Multilayer Feature Alignment and Disentanglement,
PAMI(44), No. 3, March 2022, pp. 1289-1303.
IEEE DOI 2202
Adaptation models, Machine learning, Training, Task analysis, Data models, Correlation, Training data, Generalization capability, visual recognition BibRef

Wang, H.[Hao], Bi, X.J.[Xiao-Jun],
Domain generalization and adaptation based on second-order style information,
PR(127), 2022, pp. 108595.
Elsevier DOI 2205
Domain generalization, Unsupervised domain adaptation, Two-level style normalization and restitution, Dynamic affine parameter BibRef

Yuan, M.L.[Ming-Lei], Cai, C.H.[Chun-Hao], Lu, T.[Tong], Wu, Y.R.[Yi-Rui], Xu, Q.[Qian], Zhou, S.J.[Shi-Jie],
A novel forget-update module for few-shot domain generalization,
PR(129), 2022, pp. 108704.
Elsevier DOI 2206
Few-shot classification, Domain adaptation, Few-shot domain generalization BibRef


Yüksel, O.K.[Oguz Kaan], Stich, S.U.[Sebastian U.], Jaggi, M.[Martin], Chavdarova, T.[Tatjana],
Semantic Perturbations with Normalizing Flows for Improved Generalization,
ICCV21(6599-6609)
IEEE DOI 2203
Training, Deep learning, Image coding, Perturbation methods, Semantics, Neural networks, Optimization and learning methods, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Duboudin, T.[Thomas], Dellandréa, E.[Emmanuel], Abgrall, C.[Corentin], Hénaff, G.[Gilles], Chen, L.M.[Li-Ming],
Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Single-Source Domain Generalization,
AROW21(51-60)
IEEE DOI 2112
Training, Deep learning, Heuristic algorithms, Neural networks, Training data, Benchmark testing, Prediction algorithms BibRef

Guillory, D.[Devin], Shankar, V.[Vaishaal], Ebrahimi, S.[Sayna], Darrell, T.J.[Trevor J.], Schmidt, L.[Ludwig],
Predicting with Confidence on Unseen Distributions,
ICCV21(1114-1124)
IEEE DOI 2203
Adaptation models, Uncertainty, Filtering, Veins, Training data, Focusing, Machine learning, Recognition and classification, Datasets and evaluation BibRef

Wu, G.[Guile], Gong, S.G.[Shao-Gang],
Collaborative Optimization and Aggregation for Decentralized Domain Generalization and Adaptation,
ICCV21(6464-6473)
IEEE DOI 2203
Training, Adaptation models, Data privacy, Collaboration, Benchmark testing, Predictive models, Data collection, Recognition and classification BibRef

Mansilla, L.[Lucas], Echeveste, R.[Rodrigo], Milone, D.H.[Diego H.], Ferrante, E.[Enzo],
Domain Generalization via Gradient Surgery,
ICCV21(6610-6618)
IEEE DOI 2203
Training, Surgery, Interference, Computer architecture, Picture archiving and communication systems, Data models, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Gong, Y.[Yunye], Lin, X.[Xiao], Yao, Y.[Yi], Dietterich, T.G.[Thomas G.], Divakaran, A.[Ajay], Gervasio, M.[Melinda],
Confidence Calibration for Domain Generalization under Covariate Shift,
ICCV21(8938-8947)
IEEE DOI 2203
Training, Adaptation models, Upper bound, Temperature, Linear regression, Clustering algorithms, and ethics in vision BibRef

Kim, D.[Daehee], Yoo, Y.[Youngjun], Park, S.H.[Seung-Hyun], Kim, J.[Jinkyu], Lee, J.[Jaekoo],
SelfReg: Self-supervised Contrastive Regularization for Domain Generalization,
ICCV21(9599-9608)
IEEE DOI 2203
Training, Deep learning, Computational modeling, Perturbation methods, Benchmark testing, Feature extraction, Efficient training and inference methods BibRef

Sariyildiz, M.B.[Mert Bulent], Kalantidis, Y.[Yannis], Larlus, D.[Diane], Alahari, K.[Karteek],
Concept Generalization in Visual Representation Learning,
ICCV21(9609-9619)
IEEE DOI 2203
Training, Visualization, Adaptation models, Current transformers, Search methods, Semantics, Supervised learning, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Shankar, V.[Vaishaal], Dave, A.[Achal], Roelofs, R.[Rebecca], Ramanan, D.[Deva], Recht, B.[Benjamin], Schmidt, L.[Ludwig],
Do Image Classifiers Generalize Across Time?,
ICCV21(9641-9649)
IEEE DOI 2203
Analytical models, Perturbation methods, Speech recognition, Predictive models, Benchmark testing, Robustness, Adversarial learning BibRef

Paul, S.[Soumava], Dutta, T.[Titir], Biswas, S.[Soma],
Universal Cross-Domain Retrieval: Generalizing Across Classes and Domains,
ICCV21(12036-12044)
IEEE DOI 2203
Training, Bridges, Protocols, Semantics, Task analysis, Testing, Image and video retrieval, Recognition and classification BibRef

Wang, Z.J.[Zi-Jian], Luo, Y.[Yadan], Qiu, R.[Ruihong], Huang, Z.[Zi], Baktashmotlagh, M.[Mahsa],
Learning to Diversify for Single Domain Generalization,
ICCV21(814-823)
IEEE DOI 2203
Training, Upper bound, Codes, Semantics, Benchmark testing, Optimization, Recognition and classification, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Narayanan, M.[Maruthi], Rajendran, V.[Vickram], Kimia, B.[Benjamin],
Shape-Biased Domain Generalization via Shock Graph Embeddings,
ICCV21(1295-1305)
IEEE DOI 2203
Training, Sensitivity, Shape, Electric shock, Computational modeling, Feature extraction, Recognition and classification, grouping and shape BibRef

Li, P.[Pan], Li, D.[Da], Li, W.[Wei], Gong, S.G.[Shao-Gang], Fu, Y.W.[Yan-Wei], Hospedales, T.M.[Timothy M.],
A Simple Feature Augmentation for Domain Generalization,
ICCV21(8866-8875)
IEEE DOI 2203
Training, Codes, Computational modeling, Gaussian noise, Stochastic processes, Representation learning BibRef

Tang, Z.Q.[Zhi-Qiang], Gao, Y.H.[Yun-He], Zhu, Y.[Yi], Zhang, Z.[Zhi], Li, M.[Mu], Metaxas, D.N.[Dimitris N.],
CrossNorm and SelfNorm for Generalization under Distribution Shifts,
ICCV21(52-61)
IEEE DOI 2203
Training, Bridges, Codes, Robustness, Task analysis, Recognition and classification, Vision applications and systems BibRef

Azimi, F.[Fatemeh], Palacio, S.[Sebastian], Raue, F.[Federico], Hees, J.[Jörn], Bertinetto, L.[Luca], Dengel, A.[Andreas],
Self-Supervised Test-Time Adaptation on Video Data,
WACV22(2603-2612)
IEEE DOI 2202
Adapt due to changes in video. Training, Adaptation models, Target tracking, Computational modeling, Video sequences, Training data, Vision Systems and Applications BibRef

Mangla, P.[Puneet], Chandhok, S.[Shivam], Balasubramanian, V.N.[Vineeth N.], Khan, F.S.[Fahad Shahbaz],
COCOA: Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains,
WACV22(1618-1627)
IEEE DOI 2202
Visualization, Adaptation models, Fuses, Semantics, Buildings, Benchmark testing, Transfer, Few-shot, Semi- and Un- supervised Learning Deep Learning BibRef

Kim, J.[Jin], Lee, J.Y.[Ji-Young], Park, J.[Jungin], Min, D.B.[Dong-Bo], Sohn, K.H.[Kwang-Hoon],
Self-Balanced Learning for Domain Generalization,
ICIP21(779-783)
IEEE DOI 2201
Training, Degradation, Adaptive systems, Image processing, Training data, Predictive models, Domain generalization, meta-learning BibRef

Le, H.S.[Hoang Son], Akmeliawati, R.[Rini], Carneiro, G.[Gustavo],
Combining Data Augmentation and Domain Distance Minimisation to Reduce Domain Generalisation Error,
DICTA21(01-08)
IEEE DOI 2201
Training, Adaptation models, Upper bound, Digital images, Benchmark testing, Minimization, Picture archiving and communication systems BibRef

Pandey, P.[Prashant], Raman, M.[Mrigank], Varambally, S.[Sumanth], Ap, P.[Prathosh],
Generalization on Unseen Domains via Inference-time Label-Preserving Target Projections,
CVPR21(12919-12928)
IEEE DOI 2111
Manifolds, Training, Machine learning, Extraterrestrial measurements, Data models, Pattern recognition BibRef

Li, G.R.[Guang-Rui], Kang, G.L.[Guo-Liang], Zhu, Y.[Yi], Wei, Y.C.[Yun-Chao], Yang, Y.[Yi],
Domain Consensus Clustering for Universal Domain Adaptation,
CVPR21(9752-9761)
IEEE DOI 2111
Benchmark testing, Pattern recognition BibRef

Dubey, A.[Abhimanyu], Ramanathan, V.[Vignesh], Pentland, A.[Alex], Mahajan, D.[Dhruv],
Adaptive Methods for Real-World Domain Generalization,
CVPR21(14335-14344)
IEEE DOI 2111
Training, Heart, Adaptation models, Machine learning, Benchmark testing, Predictive models BibRef

Choi, S.[Sungha], Jung, S.[Sanghun], Yun, H.[Huiwon], Kim, J.T.[Joanne T.], Kim, S.[Seungryong], Choo, J.[Jaegul],
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening,
CVPR21(11575-11585)
IEEE DOI 2111
Training, Deep learning, Image segmentation, Codes, Robustness, Pattern recognition BibRef

Fan, X.J.[Xin-Jie], Wang, Q.F.[Qi-Fei], Ke, J.J.[Jun-Jie], Yang, F.[Feng], Gong, B.Q.[Bo-Qing], Zhou, M.Y.[Ming-Yuan],
Adversarially Adaptive Normalization for Single Domain Generalization,
CVPR21(8204-8213)
IEEE DOI 2111
Training, Adaptation models, Adaptive systems, Neural networks, Benchmark testing, Tools, Data models BibRef

Huang, J.X.[Jia-Xing], Guan, D.[Dayan], Xiao, A.[Aoran], Lu, S.J.[Shi-Jian],
FSDR: Frequency Space Domain Randomization for Domain Generalization,
CVPR21(6887-6898)
IEEE DOI 2111
Training, Image segmentation, Frequency-domain analysis, Semantics, Transform coding, Aerospace electronics, Frequency conversion BibRef

Mitsuzumi, Y.[Yu], Irie, G.[Go], Ikami, D.[Daiki], Shibata, T.[Takashi],
Generalized Domain Adaptation,
CVPR21(1084-1093)
IEEE DOI 2111
Benchmark testing, Pattern recognition BibRef

Li, L.[Lei], Gao, K.[Ke], Cao, J.[Juan], Huang, Z.[Ziyao], Weng, Y.[Yepeng], Mi, X.Y.[Xiao-Yue], Yu, Z.Z.[Zheng-Ze], Li, X.Y.[Xiao-Ya], Xia, B.[Boyang],
Progressive Domain Expansion Network for Single Domain Generalization,
CVPR21(224-233)
IEEE DOI 2111
Training, Handheld computers, Computational modeling, Semantics, Transforms, Performance gain, Generators BibRef

Eguchi, S.[Shu], Nakamura, R.[Ryo], Tanaka, M.[Masaru],
Output augmentation works well without any domain knowledge,
MVA21(1-5)
DOI Link 2109
To improve generalization performance, without requiring data augmentation. Training data, Task analysis, Image classification BibRef

Borlino, F.C.[Francesco Cappio], d'Innocente, A.[Antonio], Tommasi, T.[Tatiana],
Rethinking Domain Generalization Baselines,
ICPR21(9227-9233)
IEEE DOI 2105
Deep learning, Writing, Tools, Robustness, Data models, Pattern recognition, Standards BibRef

Wang, Z.[Ziqi], Loog, M.[Marco], van Gemert, J.C.[Jan C.],
Respecting Domain Relations: Hypothesis Invariance for Domain Generalization,
ICPR21(9756-9763)
IEEE DOI 2105
Training, Estimation, Distributed databases, Probabilistic logic, Pattern recognition, Reliability, Domain generalization, invariant representation BibRef

Seo, S.[Seonguk], Suh, Y.[Yumin], Kim, D.W.[Dong-Wan], Kim, G.[Geeho], Han, J.W.[Jong-Woo], Han, B.H.[Bo-Hyung],
Learning to Optimize Domain Specific Normalization for Domain Generalization,
ECCV20(XXII:68-83).
Springer DOI 2011
BibRef

Du, Y.J.[Ying-Jun], Xu, J.[Jun], Xiong, H.[Huan], Qiu, Q.A.[Qi-Ang], Zhen, X.T.[Xian-Tong], Snoek, C.G.M.[Cees G. M.], Shao, L.[Ling],
Learning to Learn with Variational Information Bottleneck for Domain Generalization,
ECCV20(X:200-216).
Springer DOI 2011
BibRef

Chattopadhyay, P.[Prithvijit], Balaji, Y.[Yogesh], Hoffman, J.[Judy],
Learning to Balance Specificity and Invariance for In and Out of Domain Generalization,
ECCV20(IX:301-318).
Springer DOI 2011
BibRef

Wang, S.J.[Shu-Jun], Yu, L.[Lequan], Li, C.[Caizi], Fu, C.W.[Chi-Wing], Heng, P.A.[Pheng-Ann],
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization,
ECCV20(IX:159-176).
Springer DOI 2011
BibRef

Qiao, F., Zhao, L., Peng, X.,
Learning to Learn Single Domain Generalization,
CVPR20(12553-12562)
IEEE DOI 2008
Training, Task analysis, Transportation, Adaptation models, Robustness, Perturbation methods, Measurement BibRef

Truong, D.T.[Dat T.], Duong, C.N.[Chi Nhan], Luu, K.[Khoa], Tran, M.T.[Minh-Triet], Le, N.[Ngan],
Domain Generalization via Universal Non-volume Preserving Approach,
CRV20(93-100)
IEEE DOI 2006
Digits, faces, pedestrians. BibRef

Yue, X., Zhang, Y., Zhao, S., Sangiovanni-Vincentelli, A., Keutzer, K., Gong, B.,
Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data,
ICCV19(2100-2110)
IEEE DOI 2004
feature extraction, image representation, image segmentation, learning (artificial intelligence), Adaptation models BibRef

Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y., Hospedales, T.M.,
Episodic Training for Domain Generalization,
ICCV19(1446-1455)
IEEE DOI 2004
convolutional neural nets, feature extraction, generalisation (artificial intelligence), Data models BibRef

d'Innocente, A.[Antonio], Caputo, B.[Barbara],
Domain Generalization with Domain-Specific Aggregation Modules,
GCPR18(187-198).
Springer DOI 1905
BibRef

Motiian, S., Piccirilli, M., Adjeroh, D.A., Doretto, G.,
Unified Deep Supervised Domain Adaptation and Generalization,
ICCV17(5716-5726)
IEEE DOI 1802
feature extraction, image representation, learning (artificial intelligence), statistical distributions, Visualization BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Multi-Task Learning, Multiple Tasks, Transfer Learning, Domain Adaption .


Last update:Jun 27, 2022 at 12:58:02