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This paper proposes a novel batch normalization strategy for test-time adaptation.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
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Dataset shift in machine learning
Joaquin Quinonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence · 2008
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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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Diederik P Kingma and Jimmy Ba · 2015
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Domain-adversarial training of neural networks
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M Lopez · 2016
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Baochen Sun and Kate Saenko · 2016
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Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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The mapillary vistas dataset for semantic understanding of street scenes
Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulo, and Peter Kontschieder · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2018
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Xingang Pan, Ping Luo, Jianping Shi, and Xiaoou Tang · 2018
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Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2019
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Evalnorm: Estimating batch normalization statistics for evaluation
Saurabh Singh and Abhinav Shrivastava · 2019
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Four things everyone should know to improve batch normalization
Cecilia Summers and Michael J Dinneen · 2019
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Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation
Mixnorm: Test-time adaptation through online normalization estimation
Xuefeng Hu, Gokhan Uzunbas, Sirius Chen, Rui Wang, Ashish Shah, Ram Nevatia, and Ser-Nam Lim · 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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Sita: Single image test-time adaptation
Ansh Khurana, Sujoy Paul, Piyush Rai, Soma Biswas, and Gaurav Aggarwal · 2021
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Ttt++: When does self-supervised test-time training fail or thrive?
Yuejiang Liu, Parth Kothari, Bastien van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, and Alexandre Alahi · 2021
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Revisiting locally supervised learning: an alternative to end-to-end training
Yulin Wang, Zanlin Ni, Shiji Song, Le Yang, and Gao Huang · 2021
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Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, and Patrick Pérez · 2019
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Tasknorm: Rethinking batch normalization for meta-learning
John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, and Richard Turner · 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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Evaluating prediction-time batch normalization for robustness under covariate shift
Zachary Nado, Shreyas Padhy, D Sculley, Alexander D’Amour, Balaji Lakshminarayanan, and Jasper Snoek · 2020
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Test-time batch statistics calibration for covariate shift
Fuming You, Jingjing Li, and Zhou Zhao · 2021
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Parameter-free online test-time adaptation
Malik Boudiaf, Romain Mueller, Ismail Ben Ayed, and Luca Bertinetto · 2022
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Contrastive test-time adaptation
Dian Chen, Dequan Wang, Trevor Darrell, and Sayna Ebrahimi · 2022
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Improving test-time adaptation via shift-agnostic weight regularization and nearest source prototypes
Sungha Choi, Seunghan Yang, Seokeon Choi, and Sungrack Yun · 2022
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Note:robust continual test-time adaptation against temporal correlation
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Wildnet: Learning domain generalized semantic segmentation from the wild
Suhyeon Lee, Hongje Seong, Seongwon Lee, and Euntai Kim · 2022
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The norm must go on: Dynamic unsupervised domain adaptation by normalization
M Jehanzeb Mirza, Jakub Micorek, Horst Possegger, and Horst Bischof · 2022
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Continual test-time domain adaptation
Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai · 2022
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Learning instance-specific adaptation for cross-domain segmentation
Yuliang Zou, Zizhao Zhang, Chun-Liang Li, Han Zhang, Tomas Pfister, and Jia-Bin Huang · 2022
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