暂无分享,去创建一个
Seunghyun Park | Daehee Kim | Jaekoo Lee | Jinkyu Kim | Daehee Kim | Jinkyu Kim | Seunghyun Park | Jaekoo Lee
[1] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[2] Vladimir Vapnik,et al. An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.
[3] Percy Liang,et al. Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization , 2019, ArXiv.
[4] Yang Yuan,et al. Asymmetric Valleys: Beyond Sharp and Flat Local Minima , 2019, NeurIPS.
[5] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Donald A. Adjeroh,et al. Unified Deep Supervised Domain Adaptation and Generalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[7] Gilles Blanchard,et al. Generalizing from Several Related Classification Tasks to a New Unlabeled Sample , 2011, NIPS.
[8] Sethuraman Panchanathan,et al. Deep Hashing Network for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Yongxin Yang,et al. Learning to Generalize: Meta-Learning for Domain Generalization , 2017, AAAI.
[10] Donggeun Yoo,et al. Reducing Domain Gap via Style-Agnostic Networks , 2019, ArXiv.
[11] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[12] Yufei Wang,et al. Heterogeneous Domain Generalization Via Domain Mixup , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[13] Fabio Maria Carlucci,et al. Domain Generalization by Solving Jigsaw Puzzles , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Eric P. Xing,et al. Self-Challenging Improves Cross-Domain Generalization , 2020, ECCV.
[15] Andrew Gordon Wilson,et al. Averaging Weights Leads to Wider Optima and Better Generalization , 2018, UAI.
[16] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[17] Jacob Abernethy,et al. On Convergence and Stability of GANs , 2018 .
[18] Alex ChiChung Kot,et al. Domain Generalization with Adversarial Feature Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[19] MarchandMario,et al. Domain-adversarial training of neural networks , 2016 .
[20] Donggeun Yoo,et al. Reducing Domain Gap by Reducing Style Bias , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[21] David Lopez-Paz,et al. In Search of Lost Domain Generalization , 2020, ICLR.
[22] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[23] Aaron C. Courville,et al. Out-of-Distribution Generalization via Risk Extrapolation (REx) , 2020, ICML.
[24] Pietro Perona,et al. Recognition in Terra Incognita , 2018, ECCV.
[25] Daniel C. Castro,et al. Domain Generalization via Model-Agnostic Learning of Semantic Features , 2019, NeurIPS.
[26] D. Tao,et al. Deep Domain Generalization via Conditional Invariant Adversarial Networks , 2018, ECCV.
[27] Xinlei Chen,et al. Exploring Simple Siamese Representation Learning , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Yongxin Yang,et al. Deeper, Broader and Artier Domain Generalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[29] Oriol Vinyals,et al. Representation Learning with Contrastive Predictive Coding , 2018, ArXiv.
[30] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[31] Sergey Levine,et al. Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift , 2020, ArXiv.
[32] Han Zhao,et al. On Learning Invariant Representations for Domain Adaptation , 2019, ICML.
[33] Michal Valko,et al. Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning , 2020, NeurIPS.
[34] Barbara Caputo,et al. Domain Generalization with Domain-Specific Aggregation Modules , 2018, GCPR.
[35] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[36] Bo Wang,et al. Moment Matching for Multi-Source Domain Adaptation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[37] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[38] Lincan Zou,et al. Improve Unsupervised Domain Adaptation with Mixup Training , 2020, ArXiv.
[39] Ye Xu,et al. Unbiased Metric Learning: On the Utilization of Multiple Datasets and Web Images for Softening Bias , 2013, 2013 IEEE International Conference on Computer Vision.
[40] Kaiming He,et al. Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Seunghyun Park,et al. Domain Generalization Needs Stochastic Weight Averaging for Robustness on Domain Shifts , 2021, ArXiv.
[42] Bingbing Ni,et al. Adversarial Domain Adaptation with Domain Mixup , 2019, AAAI.
[43] Gilles Blanchard,et al. Domain Generalization by Marginal Transfer Learning , 2017, J. Mach. Learn. Res..
[44] David Lopez-Paz,et al. Invariant Risk Minimization , 2019, ArXiv.
[45] Bernhard Schölkopf,et al. Domain Generalization via Invariant Feature Representation , 2013, ICML.
[46] Mengjie Zhang,et al. Domain Generalization for Object Recognition with Multi-task Autoencoders , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[47] Kate Saenko,et al. Learning Deep Object Detectors from 3D Models , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[48] Kate Saenko,et al. Deep CORAL: Correlation Alignment for Deep Domain Adaptation , 2016, ECCV Workshops.
[49] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..