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Source-Free domain adaptation transits the source-trained model towards target domain without exposing the source data, trying to dispel these concerns about data privacy and security.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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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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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 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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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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An overview of gradient descent optimization algorithms
Sebastian Ruder · 2016
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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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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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Maximum classifier discrepancy for unsupervised domain adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
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When does label smoothing help?
Rafael Muller, Simon Kornblith, and Geoffrey E Hinton · 2019
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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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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
Fda: Fourier domain adaptation for semantic segmentation
Yanchao Yang and Stefano Soatto · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
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Self-labelling via simultaneous clustering and representation learning
Asano YM., Rupprecht C., and Vedaldi A · 2020
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Unsupervised multi-source domain adaptation without access to source data
Sk Miraj Ahmed, Dripta S Raychaudhuri, Sujoy Paul, Samet Oymak, and Amit K Roy-Chowdhury · 2021
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Joint clustering and discriminative feature alignment for unsupervised domain adaptation
Wanxia Deng, Qing Liao, Lingjun Zhao, Deke Guo, Gangyao Kuang, Dewen Hu, and Li Liu · 2021
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Visualizing adapted knowledge in domain transfer
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Model adaptation: Unsupervised domain adaptation without source data
Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu · 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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Unsupervised domain adaptation via structurally regularized deep clustering
Hui Tang, Ke Chen, and Kui Jia · 2020
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Dual mixup regularized learning for adversarial domain adaptation
Yuan Wu, Diana Inkpen, and Ahmed El-Roby · 2020
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Yunzhong Hou and Liang Zheng · 2021
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Domain impression: A source data free domain adaptation method
Vinod K Kurmi, Venkatesh K Subramanian, Vinay P Namboodiri, , and · 2021
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Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space
Quande Liu, Cheng Chen, Jing Qin, Qi Dou, and Pheng-Ann Heng · 2021
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Adaptive adversarial network for source-free domain adaptation
Haifeng Xia, Handong Zhao, and Zhengming Ding · 2021
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Unsupervised domain adaptation of black-box source models
Haojian Zhang, Yabin Zhang, Kui Jia, and Lei Zhang · 2021
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Contrastive test-time adaptation
Dian Chen, Dequan Wang, Trevor Darrell, and Sayna Ebrahimi · 2022
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Dine: Domain adaptation from single and multiple black-box predictors
Jian Liang, Dapeng Hu, Ran He, and Jiashi Feng · 2022
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