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Semi-supervised domain adaptation (SSDA) methods have demonstrated great potential in large-scale image classification tasks when massive labeled data are available in the source domain but very few labeled samples are provided in the target domain.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Daniel Scharstein and Richard Szeliski · 2002
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Distinctive image features from scale-invariant keypoints
David G. Lowe · 2004
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Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
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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 theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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Co-regularization based semi-supervised domain adaptation
Abhishek Kumar, Avishek Saha, and Hal Daume · 2010
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I Jordan · 2015
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Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study
Isaac Triguero, Salvador García, and Francisco Herrera · 2015
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Semi-supervised domain adaptation with subspace learning for visual recognition
Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, and Tao Mei · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Domain adaptation in computer vision applications
Gabriela Csurka · 2017
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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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L2-constrained softmax loss for discriminative face verification
Rajeev Ranjan, Carlos D Castillo, and Rama Chellappa · 2017
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Asymmetric tri-training for unsupervised domain adaptation
Kuniaki Saito, Yoshitaka Ushiku, and Tatsuya Harada · 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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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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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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Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification
Weijian Deng, Liang Zheng, Qixiang Ye, Guoliang Kang, Yi Yang, and Jianbin Jiao · 2018
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Self-ensembling for visual domain adaptation
Confidence regularized self-training
Yang Zou, Zhiding Yu, Xiaofeng Liu, BVK Kumar, and Jinsong Wang · 2018
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Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation
Xinyang Chen, Sinan Wang, Mingsheng Long, and Jianmin Wang · 2019
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Temporal cycle-consistency learning
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman · 2019
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Drop to adapt: Learning discriminative features for unsupervised domain adaptation
Seungmin Lee, Dongwan Kim, Namil Kim, and Seong-Gyun Jeong · 2019
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Transferrable prototypical networks for unsupervised domain adaptation
Yingwei Pan, Ting Yao, Yehao Li, Yu Wang, Chong-Wah Ngo, and Tao Mei · 2019
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Geoffrey French, Michal Mackiewicz, and Mark Fisher · 2018
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
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Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell · 2018
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
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Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Tom Duerig, and Vittorio Ferrari · 2018
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Domain invariant and class discriminative feature learning for visual domain adaptation
Shuang Li, Shiji Song, Gao Huang, Zhengming Ding, and Cheng Wu · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 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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Semi-supervised domain adaptation via minimax entropy
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2019
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Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation
Ruijia Xu, Guanbin Li, Jihan Yang, and Liang Lin · 2019
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d-sne: Domain adaptation using stochastic neighborhood embedding
Xiang Xu, Xiong Zhou, Ragav Venkatesan, Gurumurthy Swaminathan, and Orchid Majumder · 2019
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Gradually vanishing bridge for adversarial domain adaptation
Shuhao Cui, Shuhui Wang, Junbao Zhuo, Chi Su, Qingming Huang, and Qi Tian · 2020
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Online meta-learning for multi-source and semi-supervised domain adaptation
Da Li and Timothy Hospedales · 2020
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
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Exploring categorical regularization for domain adaptive object detection
Chang-Dong Xu, Xing-Ran Zhao, Xin Jin, and Xiu-Shen Wei · 2020
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Adversarial domain adaptation with domain mixup
Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang · 2020
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Online deep clustering for unsupervised representation learning
Xiaohang Zhan, Jiahao Xie, Ziwei Liu, Yew-Soon Ong, and Chen Change Loy · 2020
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Affinity space adaptation for semantic segmentation across domains
Wei Zhou, Yukang Wang, Jiajia Chu, Jiehua Yang, Xiang Bai, and Yongchao Xu · 2020
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