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We aim for source-free domain adaptation, where the task is to deploy a model pre-trained on source domains to target domains.
A practical bayesian framework for backpropagation networks
David JC MacKay · 1992
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Keeping the neural networks simple by minimizing the description length of the weights
Geoffrey E Hinton and Drew Van Camp · 1993
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A probability analysis on the value of unlabeled data for classification problems
T Zhang and FJ Oles · 2000
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Input-dependent regularization of conditional density models
Matthias Seeger · 2000
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Large-scale machine learning with stochastic gradient descent
Léon Bottou · 2010
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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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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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Practical variational inference for neural networks
Alex Graves · 2011
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Bayesian learning for neural networks
Radford M Neal · 2012
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Transfer feature learning with joint distribution adaptation
Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, and Philip S Yu · 2013
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Probabilistic backpropagation for scalable learning of bayesian neural networks
José Miguel Hernández-Lobato and Ryan Adams · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling · 2015
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou · 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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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Reinforced continual learning
Ju Xu and Zhanxing Zhu · 2018
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Variational continual learning
Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 2018
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Bayesian deep learning and a probabilistic perspective of generalization
Andrew G Wilson and Pavel Izmailov · 2020
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Prior choice affects ability of bayesian neural networks to identify unknowns
Daniele Silvestro and Tobias Andermann · 2020
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Adaptive component embedding for domain adaptation
Mengmeng Jing, Jidong Zhao, Jingjing Li, Lei Zhu, Yang Yang, and Heng Tao Shen · 2020
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Test-time training with self-supervision for generalization under distribution shifts
Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, and Moritz Hardt · 2020
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Augmix: A simple method to improve robustness and uncertainty under data shift
Dan Hendrycks*, Norman Mu*, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
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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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Transfer independently together: A generalized framework for domain adaptation
Jingjing Li, Ke Lu, Zi Huang, Lei Zhu, and Heng Tao Shen · 2018
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Transferable representation learning with deep adaptation networks
Mingsheng Long, Yue Cao, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
Cited alongside, same era.
Locality preserving joint transfer for domain adaptation
Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu, and Heng Tao Shen · 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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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations
Shuhao Cui, Shuhui Wang, Junbao Zhuo, Liang Li, Qingming Huang, and Qi Tian · 2020
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Bi-directional generation for unsupervised domain adaptation
Guanglei Yang, Haifeng Xia, Mingli Ding, and Zhengming Ding · 2020
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Fixbi: Bridging domain spaces for unsupervised domain adaptation
Jaemin Na, Heechul Jung, Hyung Jin Chang, and Wonjun Hwang · 2021
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Balanced open set domain adaptation via centroid alignment
Mengmeng Jing, Jingjing Li, Lei Zhu, Zhengming Ding, Ke Lu, and Yang Yang · 2021
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Exploiting the intrinsic neighborhood structure for source-free domain adaptation
Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, and Shangling Jui · 2021
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Generalized source-free domain adaptation
Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, and Shangling Jui · 2021
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Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, and Percy Liang · 2021
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A bit more bayesian: Domain-invariant learning with uncertainty
Zehao Xiao, Jiayi Shen, Xiantong Zhen, Ling Shao, and Cees G M Snoek · 2021
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Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data
Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu · 2021
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Masato Ishii and Masashi Sugiyama · 2021
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Bayesian adaptation for covariate shift
Aurick Zhou and Sergey Levine · 2021
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Robustbench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein · 2021
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Continual test-time domain adaptation
Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai · 2022
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Bayesian neural network priors revisited
Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Ratsch, Richard E Turner, Mark van der Wilk, and Laurence Aitchison · 2022
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Adversarial mixup ratio confusion for unsupervised domain adaptation
Mengmeng Jing, Lichao Meng, Jingjing Li, Lei Zhu, and Heng Tao Shen · 2022
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