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Contemporary domain adaptation methods are very effective at aligning feature distributions of source and target domains without any target supervision.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
Earlier work this paper cites.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Semi-supervised learning via generalized maximum entropy
Ayse Erkan and Yasemin Altun · 2010
Earlier work this paper cites.
Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Semi-supervised domain adaptation with instance constraints
Jeff Donahue, Judy Hoffman, Erik Rodner, Kate Saenko, and Trevor Darrell · 2013
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I Jordan · 2015
Earlier work this paper cites.
Distributional smoothing with virtual adversarial training
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, and Shin Ishii · 2015
Earlier work this paper cites.
Semi-supervised domain adaptation with subspace learning for visual recognition
Ting Yao, Yingwei Pan, Chong-Wah Ngo, Houqiang Li, and Tao Mei · 2015
Earlier work this paper cites.
Domain separation networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
Cited alongside, same era.
Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou · 2016
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
Cited alongside, same era.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
Later among the works it cites.
Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2018
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Domain adaptive faster r-cnn for object detection in the wild
Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool · 2018
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Maximum-entropy fine grained classification
Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik · 2018
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
Cited alongside, same era.
Fast generalized distillation for semi-supervised domain adaptation
Shuang Ao, Xiang Li, and Charles X Ling · 2017
Cited alongside, same era.
Autodial: Automatic domain alignment layers
Fabio Maria Cariucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, and Samuel Rota Bulò · 2017
Cited alongside, same era.
Good semi-supervised learning that requires a bad gan
Zihang Dai, Zhilin Yang, Fan Yang, William W Cohen, and Ruslan R Salakhutdinov · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
L2-constrained softmax loss for discriminative face verification
Rajeev Ranjan, Carlos D Castillo, and Rama Chellappa · 2017
Cited alongside, same era.
Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
Later among the works it cites.
Adversarial dropout regularization
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, and Kate Saenko · 2018
Later among the works it cites.
Strong-weak distribution alignment for adaptive object detection
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, and Kate Saenko · 2018
Later among the works it cites.
Maximum classifier discrepancy for unsupervised domain adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada · 2018
Later among the works it cites.
Learning from synthetic data: Addressing domain shift for semantic segmentation
Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain, Ser Nam Lim, and Rama Chellappa · 2018
Later among the works it cites.
A dirt-t approach to unsupervised domain adaptation
Rui Shu, Hung H Bui, Hirokazu Narui, and Stefano Ermon · 2018
Later among the works it cites.
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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