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Test-time adaptation (TTA) is a technique aimed at enhancing the generalization performance of models by leveraging unlabeled samples solely during prediction.
Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
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Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A Krizhevsky · 2009
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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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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Deep transfer network: Unsupervised domain adaptation
Xu Zhang, Felix Xinnan Yu, Shih-Fu Chang, and Shengjin Wang · 2015
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Deep reconstruction-classification networks for unsupervised domain adaptation
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, and Wen Li · 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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Sergey Zagoruyko and Nikos Komodakis · 2016
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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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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 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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Semi-supervised domain adaptation via minimax entropy
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2019
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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 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
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.
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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Proxymix: Proxy-based mixup training with label refinery for source-free domain adaptation
Yuhe Ding, Lijun Sheng, Jian Liang, Aihua Zheng, and Ran He · 2022
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3d common corruptions and data augmentation
Oğuzhan Fatih Kar, Teresa Yeo, Andrei Atanov, and Amir Zamir · 2022
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Introducing intermediate domains for effective self-training during test-time
Robert A Marsden, Mario Döbler, and Bin Yang · 2022
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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2020
Cited alongside, same era.
Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
Cited alongside, same era.
Robustbench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein · 2021
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 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, et al · 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.
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan · 2022
Later among the works it cites.
Mm-tta: multi-modal test-time adaptation for 3d semantic segmentation
Inkyu Shin, Yi-Hsuan Tsai, Bingbing Zhuang, Samuel Schulter, Buyu Liu, Sparsh Garg, In So Kweon, and Kuk-Jin Yoon · 2022
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Addressing distribution shift at test time in pre-trained language models
Ayush Singh and John E Ortega · 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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Out of distribution robustness with pre-trained bayesian neural networks
Xi Wang and Laurence Aitchison · 2022
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Memo: Test time robustness via adaptation and augmentation
Marvin Zhang, Sergey Levine, and Chelsea Finn · 2022
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Revisiting test time adaptation under online evaluation, 2023
Motasem Alfarra, Hani Itani, Alejandro Pardo, Shyma Alhuwaider, Merey Ramazanova, Juan C. Pérez, Zhipeng Cai, Matthias Müller, and Bernard Ghanem · 2023
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Improved test-time adaptation for domain generalization
Liang Chen, Yong Zhang, Yibing Song, Ying Shan, and Lingqiao Liu · 2023
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Decorate the newcomers: Visual domain prompt for continual test time adaptation
Yulu Gan, Xianzheng Ma, Yihang Lou, Yan Bai, Renrui Zhang, Nian Shi, and Lin Luo · 2023
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A comprehensive survey on test-time adaptation under distribution shifts
Jian Liang, Ran He, and Tieniu Tan · 2023
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Guiding pseudo-labels with uncertainty estimation for test-time adaptation
Mattia Litrico, Alessio Del Bue, and Pietro Morerio · 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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On pitfalls of test-time adaptation
Hao Zhao, Yuejiang Liu, Alexandre Alahi, and Tao Lin · 2023
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