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Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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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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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Geodesic flow kernel for unsupervised domain adaptation
Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman · 2012
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Domain adaptive neural networks for object recognition
Muhammad Ghifary, W Bastiaan Kleijn, and Mengjie Zhang · 2014
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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Deep reconstruction-classification networks for unsupervised domain adaptation
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, and Wen Li · 2016
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Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing · 2016
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Coupled generative adversarial networks
Ming-Yu Liu and Oncel Tuzel · 2016
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Playing for data: Ground truth from computer games
Stephan R Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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No more discrimination: Cross city adaptation of road scene segmenters
Yi-Hsin Chen, Wei-Yu Chen, Yu-Ting Chen, Bo-Cheng Tsai, Yu-Chiang Frank Wang, and Min Sun · 2017
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Learning to discover cross-domain relations with generative adversarial networks
Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, and Jiwon Kim · 2017
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Deep transfer learning with joint adaptation networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Secureml: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang · 2017
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Beyond sharing weights for deep domain adaptation
Artem Rozantsev, Mathieu Salzmann, and Pascal Fua · 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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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Multi-target unsupervised domain adaptation without exactly shared categories
Huanhuan Yu, Menglei Hu, and Songcan Chen · 2018
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Dualgan: Unsupervised dual learning for image-to-image translation
Zili Yi, Hao Zhang, Ping Tan, and Minglun Gong · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 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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Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation
Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary, Devis Tuia, and Nicolas Courty · 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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Contrastive adaptation network for unsupervised domain adaptation
Guoliang Kang, Lu Jiang, Yi Yang, and Alexander G Hauptmann · 2019
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Sliced wasserstein discrepancy for unsupervised domain adaptation
Chen-Yu Lee, Tanmay Batra, Mohammad Haris Baig, and Daniel Ulbricht · 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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Federated adversarial domain adaptation
Xingchao Peng, Zijun Huang, Yizhe Zhu, and Kate Saenko · 2019
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Confidence regularized self-training
Yang Zou, Zhiding Yu, Xiaofeng Liu, BVK Kumar, and Jinsong Wang · 2019
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Unsupervised multi-target domain adaptation: An information theoretic approach
Behnam Gholami, Pritish Sahu, Ognjen Rudovic, Konstantinos Bousmalis, and Vladimir Pavlovic · 2020
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Federated visual classification with real-world data distribution
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2020
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Federated semi-supervised learning with inter-client consistency
Wonyong Jeong, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang · 2020
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Bdd100k: A diverse driving dataset for heterogeneous multitask learning
Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell · 2020
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Salvaging federated learning by local adaptation
Tao Yu, Eugene Bagdasaryan, and Vitaly Shmatikov · 2020
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