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[1] Yuan Shi,et al. Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation , 2012, ICML.
[2] Michael I. Jordan,et al. Deep Transfer Learning with Joint Adaptation Networks , 2016, ICML.
[3] Yichen Wei,et al. Relation Networks for Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[4] Kate Saenko,et al. VisDA: The Visual Domain Adaptation Challenge , 2017, ArXiv.
[5] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[6] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[7] Taesung Park,et al. CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.
[8] Bin Li,et al. Deformable DETR: Deformable Transformers for End-to-End Object Detection , 2020, ICLR.
[9] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[10] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[11] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[13] Namil Kim,et al. Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[14] Michael I. Jordan,et al. Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers , 2019, ICML.
[15] Michael I. Jordan,et al. Conditional Adversarial Domain Adaptation , 2017, NeurIPS.
[16] Andrew Zisserman,et al. Video Action Transformer Network , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[17] MarchandMario,et al. Domain-adversarial training of neural networks , 2016 .
[18] David J. C. MacKay,et al. Unsupervised Classifiers, Mutual Information and 'Phantom Targets' , 1991, NIPS.
[19] Quoc V. Le,et al. Attention Augmented Convolutional Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[20] Arman Cohan,et al. Longformer: The Long-Document Transformer , 2020, ArXiv.
[21] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[22] Chunhua Shen,et al. End-to-End Video Instance Segmentation with Transformers , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[24] Junzhou Huang,et al. Progressive Feature Alignment for Unsupervised Domain Adaptation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Georg Heigold,et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2021, ICLR.
[26] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[27] Jiashi Feng,et al. Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation , 2020, ICML.
[28] Ling Shao,et al. Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions , 2021, ArXiv.
[29] Tao Xiang,et al. Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Alexander Zien,et al. Semi-Supervised Classification by Low Density Separation , 2005, AISTATS.
[31] Andreas Krause,et al. Discriminative Clustering by Regularized Information Maximization , 2010, NIPS.
[32] Masashi Sugiyama,et al. Learning Discrete Representations via Information Maximizing Self-Augmented Training , 2017, ICML.
[33] Mei Wang,et al. Deep Visual Domain Adaptation: A Survey , 2018, Neurocomputing.
[34] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Hans-Peter Kriegel,et al. Integrating structured biological data by Kernel Maximum Mean Discrepancy , 2006, ISMB.
[36] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[37] Sethuraman Panchanathan,et al. Deep Hashing Network for Unsupervised Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[39] Hui Xiong,et al. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting , 2020, AAAI.
[40] Omri Bar,et al. Video Transformer Network , 2021, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).
[41] Deng Cai,et al. Adversarial-Learned Loss for Domain Adaptation , 2020, AAAI.
[42] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[43] Yu Zhang,et al. Transfer Learning via Learning to Transfer , 2018, ICML.
[44] Jianmin Wang,et al. Transferable Attention for Domain Adaptation , 2019, AAAI.
[45] Stephen Lin,et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[46] Trevor Darrell,et al. Deep Domain Confusion: Maximizing for Domain Invariance , 2014, CVPR 2014.
[47] Nicolas Usunier,et al. End-to-End Object Detection with Transformers , 2020, ECCV.
[48] Trevor Darrell,et al. Adapting Visual Category Models to New Domains , 2010, ECCV.
[49] Tatsuya Harada,et al. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.