Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice
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Lei Zhang | Yabin Zhang | Kui Jia | Hui Tang | Bin Deng | K. Jia | Hui Tang | Lei Zhang | Yabin Zhang | Bin Deng
[1] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[2] Mehryar Mohri,et al. New Analysis and Algorithm for Learning with Drifting Distributions , 2012, ALT.
[3] Masashi Sugiyama,et al. Unsupervised Domain Adaptation Based on Source-guided Discrepancy , 2018, AAAI.
[4] Yi Lin. Multicategory Support Vector Machines, Theory, and Application to the Classification of . . . , 2003 .
[5] Jing Zhang,et al. Importance Weighted Adversarial Nets for Partial Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[6] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Shai Ben-David,et al. Understanding Machine Learning: From Theory to Algorithms , 2014 .
[8] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[9] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[10] Yi Yang,et al. Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: the Benefit of Target Expectation Maximization , 2018, ECCV.
[11] Pedro H. O. Pinheiro,et al. Unsupervised Domain Adaptation with Similarity Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[12] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[14] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[15] Michael I. Jordan,et al. Conditional Adversarial Domain Adaptation , 2017, NeurIPS.
[16] Yi Yang,et al. Contrastive Adaptation Network for Unsupervised Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Mingkui Tan,et al. Domain-Symmetric Networks for Adversarial Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Jianmin Wang,et al. Partial Adversarial Domain Adaptation , 2018, ECCV.
[19] Tianzhu Zhang,et al. GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[22] Dacheng Tao,et al. Orthogonal Deep Neural Networks , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] Kate Saenko,et al. Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation , 2018, ArXiv.
[24] Jonathan J. Hull,et al. A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[25] Tatsuya Harada,et al. Open Set Domain Adaptation by Backpropagation , 2018, ECCV.
[26] Kate Saenko,et al. VisDA: The Visual Domain Adaptation Challenge , 2017, ArXiv.
[27] Pascal Fua,et al. Beyond Sharing Weights for Deep Domain Adaptation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[29] Chen-Yu Lee,et al. Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Michael I. Jordan,et al. Deep Transfer Learning with Joint Adaptation Networks , 2016, ICML.
[31] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[32] Dumitru Erhan,et al. Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[34] Tatsuya Harada,et al. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[35] Oriol Vinyals,et al. Matching Networks for One Shot Learning , 2016, NIPS.
[36] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[37] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[38] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[39] Kate Saenko,et al. Adversarial Dropout Regularization , 2017, ICLR.
[40] Nicolas Courty,et al. Joint distribution optimal transportation for domain adaptation , 2017, NIPS.
[41] Mehryar Mohri,et al. Domain adaptation and sample bias correction theory and algorithm for regression , 2014, Theor. Comput. Sci..
[42] Trevor Darrell,et al. Simultaneous Deep Transfer Across Domains and Tasks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[43] Stefano Soatto,et al. Unsupervised Domain Adaptation via Regularized Conditional Alignment , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[44] M. Yuan,et al. Reinforced Multicategory Support Vector Machines , 2011 .
[45] Zhixin Wang,et al. Part-Aware Fine-Grained Object Categorization Using Weakly Supervised Part Detection Network , 2018, IEEE Transactions on Multimedia.
[46] Jianmin Wang,et al. Partial Transfer Learning with Selective Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[47] Nicolas Courty,et al. Optimal Transport for Domain Adaptation , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[48] Koby Crammer,et al. Analysis of Representations for Domain Adaptation , 2006, NIPS.
[49] Kui Jia,et al. Discriminative Adversarial Domain Adaptation , 2019, AAAI.
[50] Chong-Wah Ngo,et al. Transferrable Prototypical Networks for Unsupervised Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[51] Mingkui Tan,et al. Deep Multi-View Learning Using Neuron-Wise Correlation-Maximizing Regularizers , 2019, IEEE Transactions on Image Processing.
[52] 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).
[53] Bo Wang,et al. Moment Matching for Multi-Source Domain Adaptation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[54] Christian Igel,et al. A Unified View on Multi-class Support Vector Classification , 2016, J. Mach. Learn. Res..
[55] Koby Crammer,et al. A theory of learning from different domains , 2010, Machine Learning.
[56] Juergen Gall,et al. Open Set Domain Adaptation for Image and Action Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[57] Yishay Mansour,et al. Domain Adaptation: Learning Bounds and Algorithms , 2009, COLT.
[58] Jianmin Wang,et al. Transferable Attention for Domain Adaptation , 2019, AAAI.
[59] Yuchen Zhang,et al. Bridging Theory and Algorithm for Domain Adaptation , 2019, ICML.
[60] Ameet Talwalkar,et al. Foundations of Machine Learning , 2012, Adaptive computation and machine learning.
[61] Trevor Darrell,et al. Adapting Visual Category Models to New Domains , 2010, ECCV.
[62] Lei Zhang,et al. Generalization Bounds for Domain Adaptation , 2012, NIPS.
[63] V. Koltchinskii,et al. Empirical margin distributions and bounding the generalization error of combined classifiers , 2002, math/0405343.
[64] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[65] Jianmin Wang,et al. Multi-Adversarial Domain Adaptation , 2018, AAAI.
[66] Sethuraman Panchanathan,et al. Deep Hashing Network for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[67] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[68] Hong Liu,et al. Separate to Adapt: Open Set Domain Adaptation via Progressive Separation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[69] C. Villani. Optimal Transport: Old and New , 2008 .
[70] Jianmin Wang,et al. Learning to Transfer Examples for Partial Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[71] Mehryar Mohri,et al. Adaptation Algorithm and Theory Based on Generalized Discrepancy , 2014, KDD.
[72] Michael I. Jordan,et al. Unsupervised Domain Adaptation with Residual Transfer Networks , 2016, NIPS.
[73] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[74] Vinay P. Namboodiri,et al. Attending to Discriminative Certainty for Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).