Fetching the paper…
Reading the bibliography…
Unsupervised domain adaptation (UDA) methods can dramatically improve generalization on unlabeled target domains.
Nonlinear dimensionality reduction by locally linear embedding
Sam T Roweis and Lawrence K Saul · 2000
Earlier work this paper cites.
Covariate shift adaptation by importance weighted cross validation
Masashi Sugiyama, Matthias Krauledat, and Klaus-Robert MÞller · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 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.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 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.
Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I Jordan · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
Simultaneous deep transfer across domains and tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 2015
Earlier work this paper cites.
Domain separation networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan · 2016
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Earlier work this paper cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Earlier work this paper cites.
Deep reconstruction-classification networks for unsupervised domain adaptation
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, and Wen Li · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
Judy Hoffman, Dequan Wang, Fisher Yu, and Trevor Darrell · 2016
Earlier work this paper cites.
Playing for data: Ground truth from computer games
Stephan R. Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun · 2016
Cited alongside, same era.
Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
Cited alongside, same era.
Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
Cited alongside, same era.
Unsupervised pixel-level domain adaptation with generative adversarial networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan · 2017
Cited alongside, same era.
Open set domain adaptation
Pau Panareda Busto and Juergen Gall · 2017
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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.
Learning to adapt structured output space for semantic segmentation
Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang, and Manmohan Chandraker · 2018
Later among the works it cites.
Adversarial feature augmentation for unsupervised domain adaptation
Riccardo Volpi, Pietro Morerio, Silvio Savarese, and Vittorio Murino · 2018
Later among the works it cites.
Collaborative and adversarial network for unsupervised domain adaptation
Weichen Zhang, Wanli Ouyang, Wen Li, and Dong Xu · 2018
Later among the works it cites.
Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Yang Zou, Zhiding Yu, BVK Vijaya Kumar, and Jinsong Wang · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Cited alongside, same era.
Minimal-entropy correlation alignment for unsupervised deep domain adaptation
Pietro Morerio, Jacopo Cavazza, and Vittorio Murino · 2017
Cited alongside, same era.
Visda: The visual domain adaptation challenge
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
Cited alongside, same era.
Asymmetric tri-training for unsupervised domain adaptation
Kuniaki Saito, Yoshitaka Ushiku, and Tatsuya Harada · 2017
Cited alongside, same era.
Learning from simulated and unsupervised images through adversarial training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Joshua Susskind, Wenda Wang, and Russell Webb · 2017
Cited alongside, same era.
Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
Cited alongside, same era.
Learning to transfer examples for partial domain adaptation
Zhangjie Cao, Kaichao You, Mingsheng Long, Jianmin Wang, and Qiang Yang · 2019
Later among the works it cites.
Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation
Xinyang Chen, Sinan Wang, Mingsheng Long, and Jianmin Wang · 2019
Later among the works it cites.
Contrastive adaptation network for unsupervised domain adaptation
Guoliang Kang, Lu Jiang, Yi Yang, and Alexander G Hauptmann · 2019
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
Later among the works it cites.
Semi-supervised domain adaptation via minimax entropy
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2019
Later among the works it cites.
Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation
Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Mathieu Cord, and Patrick Pérez · 2019
Later among the works it cites.
Transferable normalization: Towards improving transferability of deep neural networks
Ximei Wang, Ying Jin, Mingsheng Long, Jianmin Wang, and Michael I Jordan · 2019
Later among the works it cites.
Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation
Ruijia Xu, Guanbin Li, Jihan Yang, and Liang Lin · 2019
Later among the works it cites.
Towards accurate model selection in deep unsupervised domain adaptation
Kaichao You, Ximei Wang, Mingsheng Long, and Michael Jordan · 2019
Later among the works it cites.
Confidence regularized self-training
Yang Zou, Zhiding Yu, Xiaofeng Liu, BVK Kumar, and Jinsong Wang · 2019
Later among the works it cites.
Minimum class confusion for versatile domain adaptation
Ying Jin, Ximei Wang, Mingsheng Long, and Jianmin Wang · 2020
Later among the works it cites.
Representation based complexity measures for predicting generalization in deep learning, 2020
Parth Natekar and Manik Sharma · 2020
Later among the works it cites.
Universal domain adaptation through self supervision
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, and Kate Saenko · 2020
Later among the works it cites.
Unsupervised domain adaptation via structurally regularized deep clustering
Hui Tang, Ke Chen, and Kui Jia · 2020
Later among the works it cites.