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Universal Domain Adaptation (UniDA) aims to transfer knowledge from a source domain to a target domain without any constraints on label sets.
On the translocation of masses
Leonid Kantorovitch · 1958
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Classification and analysis of multivariate observations
J MacQueen · 1967
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Diagonal equivalence to matrices with prescribed row and column sums
Richard Sinkhorn · 1967
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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The optimal partial transport problem
Alessio Figalli · 2010
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Learning with a wasserstein loss
Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya, and Tomaso A Poggio · 2015
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Unsupervised domain adaptation with residual transfer networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2016
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Visda: The visual domain adaptation challenge
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 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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Open set domain adaptation for image and action recognition
Pau Panareda Busto, Ahsan Iqbal, and Juergen Gall · 2018
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Partial adversarial domain adaptation
Zhangjie Cao, Lijia Ma, Mingsheng Long, and Jianmin Wang · 2018
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Scaling algorithms for unbalanced optimal transport problems
Lenaic Chizat, Gabriel Peyré, Bernhard Schmitzer, and François-Xavier Vialard · 2018
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Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation
Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary, Devis Tuia, and Nicolas Courty · 2018
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Deep semantic clustering by partition confidence maximisation
Jiabo Huang, Shaogang Gong, and Xiatian Zhu · 2020
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Enhanced transport distance for unsupervised domain adaptation
Mengxue Li, Yi-Ming Zhai, You-Wei Luo, Peng-Fei Ge, and Chuan-Xian Ren · 2020
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Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation
David MW Powers · 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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Scan: Learning to classify images without labels
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Open set domain adaptation by backpropagation
Kuniaki Saito, Shohei Yamamoto, Yoshitaka Ushiku, and Tatsuya Harada · 2018
Cited alongside, same era.
Importance weighted adversarial nets for partial domain adaptation
Jing Zhang, Zewei Ding, Wanqing Li, and Philip Ogunbona · 2018
Cited alongside, same era.
Self-labelling via simultaneous clustering and representation learning
YM Asano, C Rupprecht, and A Vedaldi · 2019
Cited alongside, same era.
Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
Cited alongside, same era.
Universal domain adaptation
Kaichao You, Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2019
Cited alongside, same era.
On the effectiveness of image rotation for open set domain adaptation
Silvia Bucci, Mohammad Reza Loghmani, and Tatiana Tommasi · 2020
Cited alongside, same era.
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool · 2020
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Reliable weighted optimal transport for unsupervised domain adaptation
Renjun Xu, Pelen Liu, Liyan Wang, Chao Chen, and Jindong Wang · 2020
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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Unbalanced minibatch optimal transport; applications to domain adaptation
Kilian Fatras, Thibault Séjourné, Rémi Flamary, and Nicolas Courty · 2021
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Pot: Python optimal transport
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, and Titouan Vayer · 2021
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Domain consensus clustering for universal domain adaptation
Guangrui Li, Guoliang Kang, Yi Zhu, Yunchao Wei, and Yi Yang · 2021
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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
Liang Chen, Yihang Lou, Jianzhong He, Tao Bai, and Minghua Deng · 2022
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Improving mini-batch optimal transport via partial transportation
Khai Nguyen, Dang Nguyen, Tung Pham, Nhat Ho, et al · 2022
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