Automated Synthetic-to-Real Generalization
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Anima Anandkumar | Zhangyang Wang | Zhiding Yu | Wuyang Chen | Anima Anandkumar | Zhangyang Wang | Zhiding Yu | Wuyang Chen
[1] L. Gool,et al. DLOW: Domain Flow for Adaptation and Generalization , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Pedro H. O. Pinheiro,et al. Unsupervised Domain Adaptation with Similarity Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[3] 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.
[4] Carlos D. Castillo,et al. Generate to Adapt: Aligning Domains Using Generative Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[5] Derek Hoiem,et al. Learning without Forgetting , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Misha Denil,et al. Learning to Learn without Gradient Descent by Gradient Descent , 2016, ICML.
[7] Luc Van Gool,et al. Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Marc'Aurelio Ranzato,et al. Gradient Episodic Memory for Continual Learning , 2017, NIPS.
[9] Bernhard Schölkopf,et al. Domain Generalization via Invariant Feature Representation , 2013, ICML.
[10] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[11] Yongxin Yang,et al. Learning to Generalize: Meta-Learning for Domain Generalization , 2017, AAAI.
[12] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[13] Yongxin Yang,et al. Deeper, Broader and Artier Domain Generalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[14] Jane You,et al. Feature-Level Frankenstein: Eliminating Variations for Discriminative Recognition , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Qiao Wang,et al. VirtualWorlds as Proxy for Multi-object Tracking Analysis , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Tianbao Yang,et al. Learning Attributes Equals Multi-Source Domain Generalization , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Tianlong Chen,et al. L^2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks , 2020, ArXiv.
[18] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Thomas Brox,et al. FlowNet: Learning Optical Flow with Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[20] Thomas Brox,et al. A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Trevor Darrell,et al. Deep Domain Confusion: Maximizing for Domain Invariance , 2014, CVPR 2014.
[22] Sebastian Thrun,et al. Lifelong Learning Algorithms , 1998, Learning to Learn.
[23] Tatsuya Harada,et al. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[24] Kate Saenko,et al. VisDA: The Visual Domain Adaptation Challenge , 2017, ArXiv.
[25] Misha Denil,et al. Learned Optimizers that Scale and Generalize , 2017, ICML.
[26] Taesung Park,et al. CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.
[27] Razvan Pascanu,et al. Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.
[28] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[29] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[30] Vladlen Koltun,et al. Playing for Benchmarks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[31] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[32] Yang Zou,et al. Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training , 2018, ArXiv.
[33] Vladlen Koltun,et al. Playing for Data: Ground Truth from Computer Games , 2016, ECCV.
[34] Ming-Hsuan Yang,et al. Learning to Adapt Structured Output Space for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[35] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[36] Jan Kautz,et al. Domain Stylization: A Fast Covariance Matching Framework Towards Domain Adaptation , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Xiaofeng Liu,et al. Confidence Regularized Self-Training , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[38] Gang Hua,et al. Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).
[39] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] 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).
[41] Antonio M. López,et al. The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[43] G. Evans,et al. Learning to Optimize , 2008 .
[44] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[45] Matthew Johnson-Roberson,et al. Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks? , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[46] Quoc V. Le,et al. DropBlock: A regularization method for convolutional networks , 2018, NeurIPS.
[47] Kate Saenko,et al. Deep CORAL: Correlation Alignment for Deep Domain Adaptation , 2016, ECCV Workshops.
[48] Kate Saenko,et al. Adversarial Dropout Regularization , 2017, ICLR.
[49] Jiwon Kim,et al. Continual Learning with Deep Generative Replay , 2017, NIPS.
[50] Surya Ganguli,et al. Continual Learning Through Synaptic Intelligence , 2017, ICML.
[51] Jitendra Malik,et al. Habitat: A Platform for Embodied AI Research , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[52] Luc Van Gool,et al. DLOW: Domain Flow for Adaptation and Generalization , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[53] Tianlong Chen,et al. Learning to Optimize in Swarms , 2019, NeurIPS.
[54] Xiaoou Tang,et al. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net , 2018, ECCV.