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Fully Test-Time Adaptation (TTA), which aims at adapting models to data drifts, has recently attracted wide interest.
Dataset shift in machine learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence · 2009
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.
Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
Layer normalization
Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
Earlier work this paper cites.
Dealing with multiple classes in online class imbalance learning
Shuo Wang, Leandro L. Minku, and Xin Yao · 2016
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
Earlier work this paper cites.
Batch renormalization: Towards reducing minibatch dependence in batch-normalized models
Sergey Ioffe · 2017
Earlier work this paper cites.
Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou · 2017
Earlier work this paper cites.
Visda: The visual domain adaptation challenge
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
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Group normalization
Yuxin Wu and Kaiming He · 2018
Earlier work this paper cites.
Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge J. Belongie · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Cited alongside, same era.
Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
Cited alongside, same era.
Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Jian Liang, D. Hu, and Jiashi Feng · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Mt3: Meta test-time training for self-supervised test-time adaption
Alexander Bartler, Andre Bühler, Felix Wiewel, Mario Döbler, and Bin Yang · 2022
Later among the works it cites.
Parameter-free online test-time adaptation
Malik Boudiaf, Romain Mueller, Ismail Ben Ayed, and Luca Bertinetto · 2022
Later among the works it cites.
Test-time adaptation via conjugate pseudo-labels
Sachin Goyal, Mingjie Sun, Aditi Raghunathan, and Zico Kolter · 2022
Later among the works it cites.
The norm must go on: Dynamic unsupervised domain adaptation by normalization
M. Jehanzeb Mirza, Jakub Micorek, Horst Possegger, and Horst Bischof · 2022
Later among the works it cites.
Efficient test-time model adaptation without forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shi Dong Zheng, Peilin Zhao, and Mingkui Tan · 2022
Later among the works it cites.
Memo: Test time robustness via adaptation and augmentation
Marvin Zhang, Sergey Levine, and Chelsea Finn · 2022
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Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge · 2020
Cited alongside, same era.
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
Cited alongside, same era.
The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2021
Cited alongside, same era.
Test-time classifier adjustment module for model-agnostic domain generalization
Yusuke Iwasawa and Yutaka Matsuo · 2021
Cited alongside, same era.
WILDS: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
Cited alongside, same era.
Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno A. Olshausen, and Trevor Darrell · 2021
Cited alongside, same era.
Exploiting the intrinsic neighborhood structure for source-free domain adaptation
Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, and Shangling Jui · 2021
Cited alongside, same era.
Later among the works it cites.
In search for a generalizable method for source free domain adaptation
Malik Boudiaf, Tom Denton, Bart van Merriënboer, Vincent Dumoulin, and Eleni Triantafillou · 2023
Closest in time.
Ttn: A domain-shift aware batch normalization in test-time adaptation
Hyesu Lim, Byeonggeun Kim, Jaegul Choo, and Sungha Choi · 2023
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Towards stable test-time adaptation in dynamic wild world
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, and Mingkui Tan · 2023
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Tttflow: Unsupervised test-time training with normalizing flow
David Osowiechi, Gustavo Adolfo Vargas Hakim, Mehrdad Noori, Milad Cheraghalikhani, Ismail Ben Ayed, and Christian Desrosiers · 2023
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Neuro-modulated hebbian learning for fully test-time adaptation
Yushun Tang, Ce Zhang, Heng Xu, Shuoshuo Chen, Jie Cheng, Luziwei Leng, Qinghai Guo, and Zhihai He · 2023
Closest in time.
Delta: degradation-free fully test-time adaptation
Bowen Zhao, Chen Chen, and Shutao Xia · 2023
Closest in time.