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Tong Che | Kurt Keutzer | Yoshua Bengio | Pengfei Xu | Sicheng Zhao | Wei Zhou | Bo Li | Shanghang Zhang | Yezhen Wang | Yoshua Bengio | K. Keutzer | Tong Che | Bo Li | Shanghang Zhang | Sicheng Zhao | Pengfei Xu | Yezhen Wang | Wei Zhou
[1] Mengjie Zhang,et al. Domain Generalization for Object Recognition with Multi-task Autoencoders , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[2] Yoshua Bengio,et al. Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.
[3] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[4] Geoffrey J. Gordon,et al. Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift , 2020, NeurIPS.
[5] François Laviolette,et al. Domain-Adversarial Neural Networks , 2014, ArXiv.
[6] Han Zhao,et al. Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption , 2017, AAAI.
[7] Michael I. Jordan,et al. Conditional Adversarial Domain Adaptation , 2017, NeurIPS.
[8] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[9] Jianmin Wang,et al. Multi-Adversarial Domain Adaptation , 2018, AAAI.
[10] Michael I. Jordan,et al. Deep Transfer Learning with Joint Adaptation Networks , 2016, ICML.
[11] Geoffrey French,et al. Self-ensembling for visual domain adaptation , 2017, ICLR.
[12] Bernhard Schölkopf,et al. Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.
[13] Yifan Wu,et al. Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment , 2019, ICML.
[14] Nicu Sebe,et al. Unsupervised Domain Adaptation Using Feature-Whitening and Consensus Loss , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Songtao Liu,et al. Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Alexei A. Efros,et al. Unsupervised Domain Adaptation through Self-Supervision , 2019, ArXiv.
[17] Ming Yang,et al. Conditional Generative Adversarial Network for Structured Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[18] Taesung Park,et al. CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.
[19] Xinge Zhu,et al. Adapting Object Detectors via Selective Cross-Domain Alignment , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Han Zhao,et al. On Learning Invariant Representations for Domain Adaptation , 2019, ICML.
[21] Shin Ishii,et al. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[22] Yoshua Bengio,et al. Semi-supervised Learning by Entropy Minimization , 2004, CAP.
[23] Fabio Maria Carlucci,et al. Domain Generalization by Solving Jigsaw Puzzles , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Kate Saenko,et al. Deep CORAL: Correlation Alignment for Deep Domain Adaptation , 2016, ECCV Workshops.
[25] Stefano Ermon,et al. A DIRT-T Approach to Unsupervised Domain Adaptation , 2018, ICLR.
[26] Jian Shen,et al. Wasserstein Distance Guided Representation Learning for Domain Adaptation , 2017, AAAI.
[27] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[28] Tatsuya Harada,et al. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[29] Dong Liu,et al. Robust visual domain adaptation with low-rank reconstruction , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[30] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Yi Yang,et al. Contrastive Adaptation Network for Unsupervised Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Kate Saenko,et al. Adversarial Dropout Regularization , 2017, ICLR.
[33] Kate Saenko,et al. Return of Frustratingly Easy Domain Adaptation , 2015, AAAI.
[34] Kun Zhang,et al. On Learning Invariant Representation for Domain Adaptation , 2019, ArXiv.
[35] Yizhou Wang,et al. Multi-Level Domain Adaptive Learning for Cross-Domain Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[36] Bob L. Sturm. A Simple Method to Determine if a Music Information Retrieval System is a “Horse” , 2014, IEEE Transactions on Multimedia.
[37] Alexei A. Efros,et al. Unbiased look at dataset bias , 2011, CVPR 2011.
[38] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[39] Bo Wang,et al. Moment Matching for Multi-Source Domain Adaptation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[40] Pascal Fua,et al. Non-Linear Domain Adaptation with Boosting , 2013, NIPS.
[41] Charless C. Fowlkes,et al. Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[42] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[43] Vittorio Murino,et al. Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation , 2017, ICLR.
[44] Chao Chen,et al. HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation , 2019, AAAI.
[45] Qingming Huang,et al. Towards Discriminability and Diversity: Batch Nuclear-Norm Maximization Under Label Insufficient Situations , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Lei Zhang,et al. Multi-Adversarial Faster-RCNN for Unrestricted Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[47] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[48] Chen-Yu Lee,et al. Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Kate Saenko,et al. VisDA: The Visual Domain Adaptation Challenge , 2017, ArXiv.
[50] Kurt Keutzer,et al. Multi-source Domain Adaptation for Semantic Segmentation , 2019, NeurIPS.
[51] Quoc V. Le,et al. Unsupervised Data Augmentation for Consistency Training , 2019, NeurIPS.