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Test-time adaptation (TTA) refers to adapting neural networks to distribution shifts, with access to only the unlabeled test samples from the new domain at test-time.
Unsupervised domain adaptation through self-supervision
Yu Sun, Eric Tzeng, Trevor Darrell, and Alexei A. Efros · 1909
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Probability of error of some adaptive pattern-recognition machines
H. Scudder · 1965
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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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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I. Jordan · 2015
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, and Victor Lempitsky · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Correlation alignment for unsupervised domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Visda: The visual domain adaptation challenge, 2017
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko · 2017
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Domain adaptation for semantic segmentation via class-balanced self-training
Yang Zou, Zhiding Yu, B. V. K. Vijaya Kumar, and Jinsong Wang · 2018
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Meta-learning via learned loss
Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav Sukhatme, and Franziska Meier · 2019
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Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Learning to self-train for semi-supervised few-shot classification
Xinzhe Li, Qianru Sun, Yaoyao Liu, Qin Zhou, Shibao Zheng, Tat-Seng Chua, and Bernt Schiele · 2019
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Confidence regularized self-training
Yang Zou, Zhiding Yu, Xiaofeng Liu, B. V. K. Vijaya Kumar, and Jinsong Wang · 2019
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Self-training avoids using spurious features under domain shift
Yining Chen, Colin Wei, Ananya Kumar, and Tengyu Ma · 2020
Cited alongside, same era.
Understanding self-training for gradual domain adaptation
Ananya Kumar, Tengyu Ma, and Percy Liang · 2020
Cited alongside, same era.
Model adaptation: Unsupervised domain adaptation without source data
Rui Li, Qianfen Jiao, Wenming Cao, Hau-San Wong, and Si Wu · 2020
Cited alongside, same era.
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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Consistent video depth estimation
Xuan Luo, Jia-Bin Huang, Richard Szeliski, Kevin Matzen, and Johannes Kopf · 2020
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Discovering reinforcement learning algorithms
Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado P van Hasselt, Satinder Singh, and David Silver · 2020
Sentry: Selective entropy optimization via committee consistency for unsupervised domain adaptation
Viraj Prabhu, Shivam Khare, Deeksha Kartik, and Judy Hoffman · 2021
Later among the works it cites.
Test time adaptation through perturbation robustness
Prabhu Teja Sivaprasad and François Fleuret · 2021
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In-n-out: Pre-training and self-training using auxiliary information for out-of-distribution robustness
Sang Michael Xie, Ananya Kumar, Robbie Jones, Fereshte Khani, Tengyu Ma, and Percy Liang · 2021
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Generalized source-free domain adaptation
Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, and Shangling Jui · 2021
Later among the works it cites.
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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Cited alongside, same era.
Improving robustness against common corruptions by covariate shift adaptation
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge · 2020
Cited alongside, same era.
Self-supervised test-time learning for reading comprehension
Pratyay Banerjee, Tejas Gokhale, and Chitta Baral · 2021
Cited alongside, same era.
In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2021
Cited alongside, same era.
Self-supervised policy adaptation during deployment
Nicklas Hansen, Rishabh Jangir, Yu Sun, Guillem Alenya, Pieter Abbeel, Alexei A. Efros, Lerrel Pinto, and Xiaolong Wang · 2021
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.
Evaluation of neural architectures trained with square loss vs cross-entropy in classification tasks
Like Hui and Mikhail Belkin · 2021
Cited alongside, same era.
Closest in time.
Test-time training can close the natural distribution shift performance gap in deep learning based compressed sensing
Mohammad Zalbagi Darestani, Jiayu Liu, and Reinhard Heckel · 2022
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Test-time training with masked autoencoders
Yossi Gandelsaman, Yu Sun, Xinlei Chen, and Alexei A. Efros · 2022
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Advancing momentum pseudo-labeling with conformer and initialization strategy
Yosuke Higuchi, Niko Moritz, Jonathan Le Roux, and Takaaki Hori · 2022
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Robustifying vision transformer without retraining from scratch by test-time class-conditional feature alignment
Takeshi Kojima, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Polyloss: A polynomial expansion perspective of classification loss functions
Zhaoqi Leng, Mingxing Tan, Chenxi Liu, Ekin Dogus Cubuk, Jay Shi, Shuyang Cheng, and Dragomir Anguelov · 2022
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Efficient test-time model adaptation without forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan · 2022
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If your data distribution shifts, use self-learning, 2022
Evgenia Rusak, Steffen Schneider, George Pachitariu, Luisa Eck, Peter Vincent Gehler, Oliver Bringmann, Wieland Brendel, and Matthias Bethge · 2022
Closest in time.
Test-time prompt tuning for zero-shot generalization in vision-language models
Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, and Chaowei Xiao · 2022
Closest in time.
Continual test-time domain adaptation
Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai · 2022
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Memo: Test time robustness via adaptation and augmentation
Marvin Zhang, Sergey Levine, and Chelsea Finn · 2022
Closest in time.