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

Wang, Y.[Yue], Qi, L.[Lei], Shi, Y.[Yinghuan], Gao, Y.[Yang],
Feature-Based Style Randomization for Domain Generalization,
CirSysVideo(32), No. 8, August 2022, pp. 5495-5509.
IEEE DOI 2208
Training, Data models, Adaptation models, Feature extraction, Standards, Training data, Task analysis, Domain generalization, style randomization BibRef

Ge, Z.Q.[Zhi-Qiang], Song, Z.H.[Zhi-Huan], Li, X.[Xin], Zhang, L.[Lei],
Meta conditional variational auto-encoder for domain generalization,
CVIU(222), 2022, pp. 103503.
Elsevier DOI 2209
Meta learning, Conditional variational, Domain generalization, Wasserstein distance BibRef

Christiansen, R.[Rune], Pfister, N.[Niklas], Jakobsen, M.E.[Martin Emil], Gnecco, N.[Nicola], Peters, J.[Jonas],
A Causal Framework for Distribution Generalization,
PAMI(44), No. 10, October 2022, pp. 6614-6630.
IEEE DOI 2209
Training, Predictive models, Analytical models, Mathematical model, Training data, Testing, Task analysis, Distribution generalization, domain adaptation BibRef

Du, D.P.[Da-Peng], Chen, J.W.[Jia-Wei], Li, Y.X.[Yue-Xiang], Ma, K.[Kai], Wu, G.S.[Gang-Shan], Zheng, Y.F.[Ye-Feng], Wang, L.M.[Li-Min],
Cross-Domain Gated Learning for Domain Generalization,
IJCV(130), No. 11, November 2022, pp. 2842-2857.
Springer DOI 2210
BibRef

Wang, R.Q.[Rui-Qi], Qi, L.[Lei], Shi, Y.[Yinghuan], Gao, Y.[Yang],
Better pseudo-label: Joint domain-aware label and dual-classifier for semi-supervised domain generalization,
PR(133), 2023, pp. 108987.
Elsevier DOI 2210
Semi-supervised learning, Domain generalization, Image recognition, Feature representation BibRef

Chen, S.[Sentao], Wang, L.[Lei], Hong, Z.[Zijie], Yang, X.W.[Xiao-Wei],
Domain Generalization by Joint-Product Distribution Alignment,
PR(134), 2023, pp. 109086.
Elsevier DOI 2212
Distribution alignment, Distribution divergence, Domain generalization, Feature transformation BibRef


Cha, J.[Junbum], Lee, K.[Kyungjae], Park, S.[Sungrae], Chun, S.[Sanghyuk],
Domain Generalization by Mutual-Information Regularization with Pre-trained Models,
ECCV22(XXIII:440-457).
Springer DOI 2211
BibRef

Saranrittichai, P.[Piyapat], Mummadi, C.K.[Chaithanya Kumar], Blaiotta, C.[Claudia], Munoz, M.[Mauricio], Fischer, V.[Volker],
Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domain,
ECCV22(XXV:294-309).
Springer DOI 2211
BibRef

Zhang, J.[Jian], Qi, L.[Lei], Shi, Y.[Yinghuan], Gao, Y.[Yang],
MVDG: A Unified Multi-view Framework for Domain Generalization,
ECCV22(XXVII:161-177).
Springer DOI 2211
BibRef

Nam, G.[Gilhyun], Choi, G.[Gyeongjae], Lee, K.[Kyungmin],
GCISG: Guided Causal Invariant Learning for Improved Syn-to-Real Generalization,
ECCV22(XXXIII:656-672).
Springer DOI 2211
BibRef

Zhang, C.[Chi], Xie, S.[Sirui], Jia, B.X.[Bao-Xiong], Wu, Y.N.[Ying Nian], Zhu, S.C.[Song-Chun], Zhu, Y.X.[Yi-Xin],
Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning,
ECCV22(XXIX:692-709).
Springer DOI 2211
BibRef

Fang, F.[Fen], Liang, W.Y.[Wen-Yu], Wu, Y.[Yan], Xu, Q.[Qianli], Lim, J.H.[Joo-Hwee],
Improving Generalization of Reinforcement Learning Using a Bilinear Policy Network,
ICIP22(991-995)
IEEE DOI 2211
Representation learning, Visualization, Reinforcement learning, Object detection, Games, Feature extraction, Path planning, Generalization BibRef

Xu, W.X.[Wei-Xiang], Cheng, J.[Jian],
Stacking More Linear Operations with Orthogonal Regularization to Learn Better,
ICIP22(2731-2735)
IEEE DOI 2211
Training, Deep learning, Runtime, Convolution, Stacking, Over-parameterization, Model generalization, Orthogonal regularization BibRef

Meng, R.[Rang], Li, X.F.[Xian-Feng], Chen, W.J.[Wei-Jie], Yang, S.[Shicai], Song, J.[Jie], Wang, X.C.[Xin-Chao], Zhang, L.[Lei], Song, M.L.[Ming-Li], Xie, D.[Di], Pu, S.L.[Shi-Liang],
Attention Diversification for Domain Generalization,
ECCV22(XXXIV:322-340).
Springer DOI 2211
BibRef

Kim, D.H.[Dong-Hyun], Wang, K.[Kaihong], Sclaroff, S.[Stan], Saenko, K.[Kate],
A Broad Study of Pre-training for Domain Generalization and Adaptation,
ECCV22(XXXIII:621-638).
Springer DOI 2211
BibRef

Kulinski, S.[Sean], Inouye, D.I.[David I.],
Towards Explaining Image-Based Distribution Shifts,
VDU22(4787-4791)
IEEE DOI 2210
Conferences, Pattern recognition, Task analysis BibRef

Liang, Y.Z.[Yuan-Zhi], Zhu, L.C.[Lin-Chao], Wang, X.H.[Xiao-Han], Yang, Y.[Yi],
A Simple Episodic Linear Probe Improves Visual Recognition in the Wild,
CVPR22(9549-9559)
IEEE DOI 2210
Training, Visualization, Performance gain, Time measurement, Probability distribution, Pattern recognition, retrieval BibRef

Gominski, D.[Dimitri], Gouet-Brunet, V.[Valérie], Chen, L.M.[Li-Ming],
Cross-dataset Learning for Generalizable Land Use Scene Classification,
EarthVision22(1381-1390)
IEEE DOI 2210
Training, Visualization, Image analysis, Image retrieval, Feature extraction BibRef

Cugu, I.[Ilke], Mancini, M.[Massimiliano], Chen, Y.[Yanbei], Akata, Z.[Zeynep],
Attention Consistency on Visual Corruptions for Single-Source Domain Generalization,
L3D-IVU22(4164-4173)
IEEE DOI 2210
Training, Visualization, Training data, Lighting, Benchmark testing, Picture archiving and communication systems, Data models BibRef

Lehner, A.[Alexander], Gasperini, S.[Stefano], Marcos-Ramiro, A.[Alvaro], Schmidt, M.[Michael], Mahani, M.A.N.[Mohammad-Ali Nikouei], Navab, N.[Nassir], Busam, B.[Benjamin], Tombari, F.[Federico],
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection,
CVPR22(17274-17283)
IEEE DOI 2210
Point cloud compression, Training, Shape, Object detection, Detectors, Automobiles, Navigation and autonomous driving, Adversarial attack and defense BibRef

Kim, S.W.[Seung Wook], Kreis, K.[Karsten], Li, D.Q.[Dai-Qing], Torralba, A.[Antonio], Fidler, S.[Sanja],
Polymorphic-GAN: Generating Aligned Samples across Multiple Domains with Learned Morph Maps,
CVPR22(10620-10630)
IEEE DOI 2210
Training, Geometry, Image segmentation, Shape, Generative adversarial networks, Generators, Transfer/low-shot/long-tail learning BibRef

Huang, Z.Y.[Ze-Yi], Wang, H.[Haohan], Huang, D.[Dong], Lee, Y.J.[Yong Jae], Xing, E.P.[Eric P.],
The Two Dimensions of Worst-case Training and Their Integrated Effect for Out-of-domain Generalization,
CVPR22(9621-9631)
IEEE DOI 2210
Training, Representation learning, Correlation, Merging, Force, Robustness, Self- semi- meta- Representation learning BibRef

Bayasi, N.[Nourhan], Hamarneh, G.[Ghassan], Garbi, R.[Rafeef],
BoosterNet: Improving Domain Generalization of Deep Neural Nets using Culpability-Ranked Features,
CVPR22(528-538)
IEEE DOI 2210
Training, Deep learning, Neural networks, Mission critical systems, Measurement uncertainty, Imaging, Network architecture, Efficient learning and inferences BibRef

Wan, C.[Chaoqun], Shen, X.[Xu], Zhang, Y.G.[Yong-Gang], Yin, Z.H.[Zhi-Heng], Tian, X.[Xinmei], Gao, F.[Feng], Huang, J.Q.[Jian-Qiang], Hua, X.S.[Xian-Sheng],
Meta Convolutional Neural Networks for Single Domain Generalization,
CVPR22(4672-4681)
IEEE DOI 2210
Convolutional codes, Representation learning, Visualization, Image recognition, Benchmark testing, Image representation, Deep learning architectures and techniques BibRef

Zhang, X.X.[Xing-Xuan], Zhou, L.J.[Lin-Jun], Xu, R.Z.[Ren-Zhe], Cui, P.[Peng], Shen, Z.[Zheyan], Liu, H.X.[Hao-Xin],
Towards Unsupervised Domain Generalization,
CVPR22(4900-4910)
IEEE DOI 2210
Representation learning, Analytical models, Protocols, Computational modeling, Data models, Pattern recognition, Vision applications and systems BibRef

Harary, S.[Sivan], Schwartz, E.[Eli], Arbelle, A.[Assaf], Staar, P.[Peter], Abu-Hussein, S.[Shady], Amrani, E.[Elad], Herzig, R.[Roei], Alfassy, A.[Amit], Giryes, R.[Raja], Kuehne, H.[Hilde], Katabi, D.[Dina], Saenko, K.[Kate], Feris, R.[Rogerio], Karlinsky, L.[Leonid],
Unsupervised Domain Generalization by Learning a Bridge Across Domains,
CVPR22(5270-5280)
IEEE DOI 2210
Bridges, Training, Representation learning, Visualization, Semantics, Self-supervised learning, Visual systems, Recognition: detection, Representation learning BibRef

Zhu, W.[Wei], Lu, L.[Le], Xiao, J.[Jing], Han, M.[Mei], Luo, J.B.[Jie-Bo], Harrison, A.P.[Adam P.],
Localized Adversarial Domain Generalization,
CVPR22(7098-7108)
IEEE DOI 2210
Deep learning, Training data, Games, Benchmark testing, Maintenance engineering, Pattern recognition, Machine learning BibRef

Chen, C.Q.[Chao-Qi], Li, J.[Jiongcheng], Han, X.G.[Xiao-Guang], Liu, X.Q.[Xiao-Qing], Yu, Y.Z.[Yi-Zhou],
Compound Domain Generalization via Meta-Knowledge Encoding,
CVPR22(7109-7119)
IEEE DOI 2210
Representation learning, Semantics, Prototypes, Object detection, Benchmark testing, Encoding, Pattern recognition, Representation learning BibRef

Kang, J.[Juwon], Lee, S.[Sohyun], Kim, N.[Namyup], Kwak, S.[Suha],
Style Neophile: Constantly Seeking Novel Styles for Domain Generalization,
CVPR22(7120-7130)
IEEE DOI 2210
Greedy algorithms, Training, Representation learning, Computational modeling, Training data, Benchmark testing, retrieval BibRef

Zhang, H.L.[Han-Lin], Zhang, Y.F.[Yi-Fan], Liu, W.Y.[Wei-Yang], Weller, A.[Adrian], Schölkopf, B.[Bernhard], Xing, E.P.[Eric P.],
Towards Principled Disentanglement for Domain Generalization,
CVPR22(8014-8024)
IEEE DOI 2210
Training, Correlation, Semantics, Training data, Machine learning, Benchmark testing, Transfer/low-shot/long-tail learning, privacy and ethics in vision BibRef

Zhang, Y.[Yabin], Li, M.[Minghan], Li, R.[Ruihuang], Jia, K.[Kui], Zhang, L.[Lei],
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization,
CVPR22(8025-8035)
IEEE DOI 2210
Visualization, Histograms, Image recognition, Costs, Statistical analysis, Pattern recognition, Statistical methods BibRef

Lv, F.[Fangrui], Liang, J.[Jian], Li, S.[Shuang], Zang, B.[Bin], Liu, C.H.[Chi Harold], Wang, Z.[Ziteng], Liu, D.[Di],
Causality Inspired Representation Learning for Domain Generalization,
CVPR22(8036-8046)
IEEE DOI 2210
Representation learning, Data models, Pattern recognition, Classification algorithms, Self- semi- meta- unsupervised learning BibRef

Zhang, J.W.[Jia-Wei], Wang, X.[Xiang], Bai, X.[Xiao], Wang, C.[Chen], Huang, L.[Lei], Chen, Y.M.[Yi-Min], Gu, L.[Lin], Zhou, J.[Jun], Harada, T.[Tatsuya], Hancock, E.R.[Edwin R.],
Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective,
CVPR22(12991-13001)
IEEE DOI 2210
Training, Codes, Training data, Decorrelation, Pattern matching, 3D from multi-view and sensors, Navigation and autonomous driving BibRef

Liu, B.Y.[Bi-Yang], Yu, H.M.[Hui-Min], Qi, G.D.[Guo-Dong],
GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature,
CVPR22(13002-13011)
IEEE DOI 2210
Training, Costs, Image color analysis, Multitasking, 3D from multi-view and sensors BibRef

Chuah, W.Q.[Wei-Qin], Tennakoon, R.[Ruwan], Hoseinnezhad, R.[Reza], Bab-Hadiashar, A.[Alireza], Suter, D.[David],
ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networks,
CVPR22(13012-13022)
IEEE DOI 2210
Sensitivity, Perturbation methods, Semantics, Feature extraction, Robustness, Sensors, 3D from multi-view and sensors BibRef

Galstyan, T.[Tigran], Harutyunyan, H.[Hrayr], Khachatrian, H.[Hrant], Steeg, G.V.[Greg Ver], Galstyan, A.[Aram],
Failure Modes of Domain Generalization Algorithms,
CVPR22(19055-19064)
IEEE DOI 2210
Training, Representation learning, Machine learning algorithms, Training data, Focusing, Data models, Machine learning, Datasets and evaluation BibRef

Nazari, N.H.[Narges Honarvar], Kovashka, A.[Adriana],
The Role of Shape for Domain Generalization on Sparsely-Textured Images,
SketchDL22(5116-5126)
IEEE DOI 2210
Bridges, Shape, Transforms, Robustness, Pattern recognition 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, 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
See also Study of RobustNet, a Domain Generalization Method for Semantic Segmentation, A. 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.Q.[Zi-Qi], 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:Dec 4, 2022 at 15:58:45