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Xu, Z.[Zheng],
Xu, D.[Dong],
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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
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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
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Kim, H.[Hoki],
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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
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Li, H.L.[Hao-Liang],
Wang, S.Q.[Shi-Qi],
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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],
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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],
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Meta conditional variational auto-encoder for domain generalization,
CVIU(222), 2022, pp. 103503.
Elsevier DOI
2209
Meta learning, Conditional variational, Domain generalization,
Wasserstein distance
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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
Saengkyongam, S.[Sorawit],
Thams, N.[Nikolaj],
Peters, J.[Jonas],
Pfister, N.[Niklas],
Invariant Policy Learning: A Causal Perspective,
PAMI(45), No. 7, July 2023, pp. 8606-8620.
IEEE DOI
2306
Training, Visualization, Reinforcement learning, Random variables,
Particle measurements, Heuristic algorithms, off-policy learning
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
Tian, C.X.[Chris Xing],
Li, H.L.[Hao-Liang],
Xie, X.F.[Xiao-Fei],
Liu, Y.[Yang],
Wang, S.Q.[Shi-Qi],
Neuron Coverage-Guided Domain Generalization,
PAMI(45), No. 1, January 2023, pp. 1302-1311.
IEEE DOI
2212
Neurons, Training, Task analysis, Semantics, Computer bugs, Training data,
Software, Gradient similarity, neuron coverage, out-of-distribution
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Segu, M.[Mattia],
Tonioni, A.[Alessio],
Tombari, F.[Federico],
Batch normalization embeddings for deep domain generalization,
PR(135), 2023, pp. 109115.
Elsevier DOI
2212
Domain generalization, Domain representation learning,
Learning from multiple sources
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Wang, Y.Q.[Yun-Qi],
Liu, F.[Furui],
Chen, Z.T.[Zhi-Tang],
Wu, Y.C.[Yik-Chung],
Hao, J.[Jianye],
Chen, G.Y.[Guang-Yong],
Heng, P.A.[Pheng-Ann],
Contrastive-ACE: Domain Generalization Through Alignment of Causal
Mechanisms,
IP(32), 2023, pp. 235-250.
IEEE DOI
2301
Training, Feature extraction, Data models, Task analysis, Training data,
Predictive models, Optimization, Causal inference, deep learning
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Yuan, J.K.[Jun-Kun],
Ma, X.[Xu],
Chen, D.F.[De-Fang],
Kuang, K.[Kun],
Wu, F.[Fei],
Lin, L.F.[Lan-Fen],
Domain-Specific Bias Filtering for Single Labeled Domain Generalization,
IJCV(131), No. 2, February 2023, pp. 552-571.
Springer DOI
2301
BibRef
Liu, Y.J.[Ya-Jing],
Xiong, Z.W.[Zhi-Wei],
Li, Y.[Ya],
Tian, X.[Xinmei],
Zha, Z.J.[Zheng-Jun],
Domain Generalization Via Encoding and Resampling in a Unified Latent
Space,
MultMed(25), 2023, pp. 126-139.
IEEE DOI
2301
Feature extraction, Training, Perturbation methods, Encoding,
Gaussian distribution, Aerospace electronics, Data mining,
adversarial examples
BibRef
Yang, Y.H.[Yan-Hua],
Zhang, X.Z.[Xiao-Zhe],
Yang, M.[Muli],
Deng, C.[Cheng],
Adaptive Bias-Aware Feature Generation for Generalized Zero-Shot
Learning,
MultMed(25), 2023, pp. 280-290.
IEEE DOI
2301
Visualization, Semantics, Generators, Training,
Generative adversarial networks, Data models, Benchmark testing,
bias problem
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Luna, E.[Elena],
SanMiguel, J.C.[Juan C.],
Martínez, J.M.[José M.],
Carballeira, P.[Pablo],
Graph Neural Networks for Cross-Camera Data Association,
CirSysVideo(33), No. 2, February 2023, pp. 589-601.
IEEE DOI
2302
Cameras, Task analysis, Image edge detection, Message passing,
Graph neural networks, Feature extraction, Data association,
message passing network
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Zhou, K.Y.[Kai-Yang],
Liu, Z.[Ziwei],
Qiao, Y.[Yu],
Xiang, T.[Tao],
Loy, C.C.[Chen Change],
Domain Generalization: A Survey,
PAMI(45), No. 4, April 2023, pp. 4396-4415.
IEEE DOI
2303
Survey, Domain Generalization. Data models, Speech recognition, Adaptation models,
Face recognition, Soft sensors, Handwriting recognition, machine learning
BibRef
Xia, H.F.[Hai-Feng],
Jing, T.[Taotao],
Ding, Z.M.[Zheng-Ming],
Generative Inference Network for Imbalanced Domain Generalization,
IP(32), 2023, pp. 1694-1704.
IEEE DOI
2303
Feature extraction, Training, Task analysis, Semantics, Robustness,
Data models, Visualization, knowledge transfer
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Xu, Q.W.[Qin-Wei],
Zhang, R.P.[Rui-Peng],
Fan, Z.Q.[Zi-Qing],
Wang, Y.F.[Yan-Feng],
Wu, Y.Y.[Yi-Yan],
Zhang, Y.[Ya],
Fourier-based augmentation with applications to domain generalization,
PR(139), 2023, pp. 109474.
Elsevier DOI
2304
Domain shift, Domain generalization,
Fourier-based augmentation, Consistency training
BibRef
Zhu, S.[Sihan],
Wu, C.[Chen],
Du, B.[Bo],
Zhang, L.P.[Liang-Pei],
Style and content separation network for remote sensing image
cross-scene generalization,
PandRS(201), 2023, pp. 1-11.
Elsevier DOI
2307
Cross-scene classification, Deep learning,
Domain generalization, Style manipulation
BibRef
Zhou, K.Y.[Kai-Yang],
Loy, C.C.[Chen Change],
Liu, Z.W.[Zi-Wei],
Semi-Supervised Domain Generalization with Stochastic StyleMatch,
IJCV(131), No. 9, September 2023, pp. 2377-2387.
Springer DOI
2308
BibRef
Zhang, X.[Xin],
Chen, Y.C.[Ying-Cong],
Adaptive Domain Generalization Via Online Disagreement Minimization,
IP(32), 2023, pp. 4247-4258.
IEEE DOI
2308
Adaptation models, Feature extraction, Training, Predictive models,
Data models, Minimization, Entropy, Domain shift,
consistency regularization
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Zhang, L.[Lei],
Du, Y.J.[Ying-Jun],
Shen, J.Y.[Jia-Yi],
Zhen, X.T.[Xian-Tong],
Learning to Learn With Variational Inference for Cross-Domain Image
Classification,
MultMed(25), 2023, pp. 3319-3328.
IEEE DOI
2309
BibRef
Du, Y.J.[Ying-Jun],
Xu, J.[Jun],
Xiong, H.[Huan],
Qiu, Q.A.[Qi-Ang],
Zhen, X.T.[Xian-Tong],
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Shao, L.[Ling],
Learning to Learn with Variational Information Bottleneck for Domain
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ECCV20(X:200-216).
Springer DOI
2011
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Chuah, W.Q.[Wei-Qin],
Tennakoon, R.[Ruwan],
Hoseinnezhad, R.[Reza],
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Bab-Hadiashar, A.[Alireza],
An Information-Theoretic Method to Automatic Shortcut Avoidance and
Domain Generalization for Dense Prediction Tasks,
PAMI(45), No. 9, September 2023, pp. 10615-10631.
IEEE DOI
2309
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Sultana, M.[Maryam],
Naseer, M.[Muzammal],
Khan, M.H.[Muhammad Haris],
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Self-distilled Vision Transformer for Domain Generalization,
ACCV22(II:273-290).
Springer DOI
2307
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Mondal, B.[Biswajit],
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SEIC: Semantic Embedding with Intermediate Classes for Zero-shot Domain
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ACCV22(V:333-350).
Springer DOI
2307
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Li, A.[Aodi],
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Fan, S.[Shuo],
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Learning Common and Specific Visual Prompts for Domain Generalization,
ACCV22(VI:578-593).
Springer DOI
2307
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Evaluating Domain Generalization in Kitchen Utensils Classification,
IbPRIA23(108-118).
Springer DOI
2307
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Chen, H.R.[Huan-Ran],
Shao, S.T.[Shi-Tong],
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Bootstrap Generalization Ability from Loss Landscape Perspective,
CiV22(500-517).
Springer DOI
2304
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Bajcsy, P.[Peter],
Majurski, M.[Michael],
Cleveland IV, T.E.[Thomas E.],
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Characterization of AI Model Configurations for Model Reuse,
BioImage22(454-469).
Springer DOI
2304
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Domain-conditioned Normalization for Test-time Domain Generalization,
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Springer DOI
2304
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Modselect: Automatic Modality Selection for Synthetic-to-real Domain
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OutDistri22(326-346).
Springer DOI
2304
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Domain Generalization with Global Sample Mixup,
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Springer DOI
2304
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Liang, S.Y.[Si-Yuan],
Wang, L.H.[Li-Hua],
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Tang, Y.[Yao],
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Simpledg: Simple Domain Generalization Baseline Without Bells and
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CiV22(477-487).
Springer DOI
2304
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Wolfinger, B.[Brett],
Bukowski, J.[Julia],
Unberath, M.[Mathias],
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Mapping DNN Embedding Manifolds for Network Generalization Prediction,
WACV23(6513-6522)
IEEE DOI
2302
Manifolds, Visualization, Image analysis, Melanoma, Metadata,
Applications: Robotics, Biomedical/healthcare/medicine
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Chen, T.[Tianle],
Baktashmotlagh, M.[Mahsa],
Wang, Z.J.[Zi-Jian],
Salzmann, M.[Mathieu],
Center-aware Adversarial Augmentation for Single Domain
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WACV23(4146-4154)
IEEE DOI
2302
Training, Perturbation methods, Semantics, Training data,
Benchmark testing, Feature extraction,
and algorithms (including transfer)
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Li, Y.[Yumeng],
Zhang, D.[Dan],
Keuper, M.[Margret],
Khoreva, A.[Anna],
Intra-Source Style Augmentation for Improved Domain Generalization,
WACV23(509-519)
IEEE DOI
2302
Training, Semantic segmentation, Semantics, Layout, Training data,
Predictive models, Network architecture,
image and video synthesis
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Gokhale, T.[Tejas],
Anirudh, R.[Rushil],
Thiagarajan, J.J.[Jayaraman J.],
Kailkhura, B.[Bhavya],
Baral, C.[Chitta],
Yang, Y.Z.[Ye-Zhou],
Improving Diversity with Adversarially Learned Transformations for
Domain Generalization,
WACV23(434-443)
IEEE DOI
2302
Training, Perturbation methods, Neural networks, Transforms,
Benchmark testing, Performance gain, and algorithms (including transfer)
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Chen, J.M.[Jun-Ming],
Jiang, M.[Meirui],
Dou, Q.[Qi],
Chen, Q.F.[Qi-Feng],
Federated Domain Generalization for Image Recognition via
Cross-Client Style Transfer,
WACV23(361-370)
IEEE DOI
2302
Training, Data privacy, Image recognition, Federated learning,
Data models, Picture archiving and communication systems,
ethical computer vision
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Nguyen, T.[Thuan],
Lyu, B.Y.[Bo-Yang],
Ishwar, P.[Prakash],
Scheutz, M.[Matthias],
Aeron, S.[Shuchin],
Conditional entropy minimization principle for learning domain
invariant representation features,
ICPR22(3000-3006)
IEEE DOI
2212
Mixture models, Filtering algorithms, Minimization,
Feature extraction, Entropy, Filtering theory, Classification algorithms
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Liu, X.X.[Xi-Xi],
Staudt, D.,
Lin, C.T.[Che-Tsung],
Zach, C.[Christopher],
Effortless Training of Joint Energy-Based Models with Sliced Score
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ICPR22(2643-2649)
IEEE DOI
2212
Training, Analytical models, Uncertainty, Stochastic processes,
Predictive models, Logic gates, Calibration
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Tao, C.F.[Chao-Fan],
Wong, N.[Ngai],
ODG-Q: Robust Quantization via Online Domain Generalization,
ICPR22(1822-1828)
IEEE DOI
2212
Training, Quantization (signal), Costs, Perturbation methods,
Neural networks, Closed box, Robustness
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Frikha, A.[Ahmed],
Krompaß, D.[Denis],
Tresp, V.[Volker],
Discovery of New Multi-Level Features for Domain Generalization via
Knowledge Corruption,
ICPR22(1871-187)
IEEE DOI
2212
Training, Machine learning, Benchmark testing, Data models
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Zhang, W.Y.[Wen-Yu],
Ragab, M.[Mohamed],
Foo, C.S.[Chuan-Sheng],
Domain Generalization via Selective Consistency Regularization for
Time Series Classification,
ICPR22(2149-2156)
IEEE DOI
2212
Representation learning, Training, Time series analysis,
Feature extraction, Data models, Calibration
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Chen, T.L.[Tian-Le],
Baktashmotlagh, M.[Mahsa],
Salzmann, M.[Mathieu],
Contrastive Class-aware Adaptation for Domain Generalization,
ICPR22(4871-4876)
IEEE DOI
2212
Training, Heart, Semantic segmentation, Semantics, Predictive models,
Feature extraction, Market research
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Cha, J.[Junbum],
Lee, K.[Kyungjae],
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Chun, S.[Sanghyuk],
Domain Generalization by Mutual-Information Regularization with
Pre-trained Models,
ECCV22(XXIII:440-457).
Springer DOI
2211
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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
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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
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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.L.[Qian-Li],
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
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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
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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
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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
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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.H.[Hao-Han],
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
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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
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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.H.[Rui-Huang],
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.T.[Zi-Teng],
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.H.[Rui-Hong],
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.Y.[Zi-Yao],
Weng, Y.P.[Ye-Peng],
Mi, X.Y.[Xiao-Yue],
Yu, Z.Z.[Zheng-Ze],
Li, X.Y.[Xiao-Ya],
Xia, B.Y.[Bo-Yang],
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
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 .