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Anima Anandkumar | Zhangyang Wang | Shalini De Mello | Sifei Liu | Zhiding Yu | Jose M. Alvarez | Wuyang Chen | Anima Anandkumar | J. Álvarez | Zhangyang Wang | Zhiding Yu | Sifei Liu | Wuyang Chen
[1] Phillip Isola,et al. Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere , 2020, ICML.
[2] Stella X. Yu,et al. Unsupervised Feature Learning via Non-parametric Instance Discrimination , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[3] Razvan Pascanu,et al. Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.
[4] Kate Saenko,et al. VisDA: The Visual Domain Adaptation Challenge , 2017, ArXiv.
[5] Yoshua Bengio,et al. Learning deep representations by mutual information estimation and maximization , 2018, ICLR.
[6] Luc Van Gool,et al. ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[7] D. W. Scott,et al. Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .
[8] Yongxin Yang,et al. Deeper, Broader and Artier Domain Generalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[9] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[10] Kaiming He,et al. Improved Baselines with Momentum Contrastive Learning , 2020, ArXiv.
[11] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Le Song,et al. Learning towards Minimum Hyperspherical Energy , 2018, NeurIPS.
[13] Oriol Vinyals,et al. Representation Learning with Contrastive Predictive Coding , 2018, ArXiv.
[14] Laurens van der Maaten,et al. Self-Supervised Learning of Pretext-Invariant Representations , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[16] Bolei Zhou,et al. Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Phillip Isola,et al. Contrastive Multiview Coding , 2019, ECCV.
[19] Derek Hoiem,et al. Learning without Forgetting , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] Cordelia Schmid,et al. What makes for good views for contrastive learning , 2020, NeurIPS.
[21] Jitendra Malik,et al. Habitat: A Platform for Embodied AI Research , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[22] Tianbao Yang,et al. Learning Attributes Equals Multi-Source Domain Generalization , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Mohammad Norouzi,et al. Big Self-Supervised Models are Strong Semi-Supervised Learners , 2020, NeurIPS.
[24] Ting Chen,et al. Robust Pre-Training by Adversarial Contrastive Learning , 2020, NeurIPS.
[25] Yonglong Tian,et al. Contrastive Representation Distillation , 2019, ICLR.
[26] Jianxiong Xiao,et al. 3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Ali Razavi,et al. Data-Efficient Image Recognition with Contrastive Predictive Coding , 2019, ICML.
[28] Xiaoou Tang,et al. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net , 2018, ECCV.
[29] Surya Ganguli,et al. Continual Learning Through Synaptic Intelligence , 2017, ICML.
[30] Quoc V. Le,et al. Randaugment: Practical automated data augmentation with a reduced search space , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[31] Vladlen Koltun,et al. Playing for Data: Ground Truth from Computer Games , 2016, ECCV.
[32] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[33] Kaiming He,et al. Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[35] Alberto L. Sangiovanni-Vincentelli,et al. Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[36] Tomas Pfister,et al. Learning from Simulated and Unsupervised Images through Adversarial Training , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Bernhard Schölkopf,et al. Domain Generalization via Invariant Feature Representation , 2013, ICML.
[38] Yi Yang,et al. Self-produced Guidance for Weakly-supervised Object Localization , 2018, ECCV.
[39] George Papandreou,et al. Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.
[40] Matthias Bethge,et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness , 2018, ICLR.