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Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to the target domain without any prior knowledge about the label set.
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Robust face recognition via sparse representation
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Imagenet classification with deep convolutional neural networks
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Yuejie Chi and Fatih Porikli · 2013
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Collaborative representation for classification, sparse or non-sparse?
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Unsupervised domain adaptation by backpropagation
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Open set domain adaptation
Pau Panareda Busto and Juergen Gall · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Partial transfer learning with selective adversarial networks
Zhangjie Cao, Mingsheng Long, Jianmin Wang, and Michael I Jordan · 2018
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Partial adversarial domain adaptation
Zhangjie Cao, Lijia Ma, Mingsheng Long, and Jianmin Wang · 2018
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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A joint optimization framework of low-dimensional projection and collaborative representation for discriminative classification
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Visda: A synthetic-to-real benchmark for visual domain adaptation
Xingchao Peng, Ben Usman, Neela Kaushik, Dequan Wang, Judy Hoffman, and Kate Saenko · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Open set domain adaptation by backpropagation
Kuniaki Saito, Shohei Yamamoto, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Video action transformer network
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Separate to adapt: Open set domain adaptation via progressive separation
Hong Liu, Zhangjie Cao, Mingsheng Long, Jianmin Wang, and Qiang Yang · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Universal domain adaptation
Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2019
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On the effectiveness of image rotation for open set domain adaptation
Silvia Bucci, Mohammad Reza Loghmani, and Tatiana Tommasi · 2020
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End-to-end object detection with transformers
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Towards out-of-distribution generalization: A survey
Zheyan Shen, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, and Peng Cui · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Are convolutional neural networks or transformers more like human vision?
Shikhar Tuli, Ishita Dasgupta, Erin Grant, and Thomas L Griffiths · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2021
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End-to-end video instance segmentation with transformers
Yuqing Wang, Zhaoliang Xu, Xinlong Wang, Chunhua Shen, Baoshan Cheng, Hao Shen, and Huaxia Xia · 2021
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Learning to detect open classes for universal domain adaptation
Bo Fu, Zhangjie Cao, Mingsheng Long, and Jianmin Wang · 2020
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The origins and prevalence of texture bias in convolutional neural networks
Katherine Hermann, Ting Chen, and Simon Kornblith · 2020
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Universal source-free domain adaptation
Jogendra Nath Kundu, Naveen Venkat, R Venkatesh Babu, et al · 2020
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A balanced and uncertainty-aware approach for partial domain adaptation
Jian Liang, Yunbo Wang, Dapeng Hu, Ran He, and Jiashi Feng · 2020
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A sample selection approach for universal domain adaptation
Omri Lifshitz and Lior Wolf · 2020
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Universal domain adaptation through self supervision
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, and Kate Saenko · 2020
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Cdtrans: Cross-domain transformer for unsupervised domain adaptation
Tongkun Xu, Weihua Chen, Pichao Wang, Fan Wang, Hao Li, and Rong Jin · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al · 2021
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Unified optimal transport framework for universal domain adaptation
Wanxing Chang, Ye Shi, Hoang Duong Tuan, and Jingya Wang · 2022
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Mutual nearest neighbor contrast and hybrid prototype self-training for universal domain adaptation
Liang Chen, Qianjin Du, Yihang Lou, Jianzhong He, Tao Bai, and Minghua Deng · 2022
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Evidential neighborhood contrastive learning for universal domain adaptation
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Geometric anchor correspondence mining with uncertainty modeling for universal domain adaptation
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Subsidiary prototype alignment for universal domain adaptation
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S2rl: Do we really need to perceive all states in deep multi-agent reinforcement learning?
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Attention-based cross-layer domain alignment for unsupervised domain adaptation
Xu Ma, Junkun Yuan, Yen-wei Chen, Ruofeng Tong, and Lanfen Lin · 2022
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Safe self-refinement for transformer-based domain adaptation
Tao Sun, Cheng Lu, Tianshuo Zhang, and Haibin Ling · 2022
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Fairness-aware contrastive learning with partially annotated sensitive attributes
Fengda Zhang, Kun Kuang, Long Chen, Yuxuan Liu, Chao Wu, and Jun Xiao · 2022
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Domain generalized few-shot image classification via meta regularization network
Min Zhang, Siteng Huang, and Donglin Wang · 2022
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A prototype network enhanced relation semantic representation for few-shot relation extraction
Haitao He, Haoran Niu, Jianzhou Feng, Qian Wang, and Qikai Wei · 2023
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Dbaformer: A double-branch attention transformer for long-term time series forecasting
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Ideal: Toward high-efficiency device-cloud collaborative and dynamic recommendation system
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Duet: A tuning-free device-cloud collaborative parameters generation framework for efficient device model generalization
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Tvt: Transferable vision transformer for unsupervised domain adaptation
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Domain-specific bias filtering for single labeled domain generalization
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Instrumental variable-driven domain generalization with unobserved confounders
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