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Continual test-time adaptation (cTTA) methods are designed to facilitate the continual adaptation of models to dynamically changing real-world environments where computational resources are limited.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Domain adaptation via transfer component analysis
Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang · 2010
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 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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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
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Visual domain adaptation: A survey of recent advances
Vishal M Patel, Raghuraman Gopalan, Ruonan Li, and Rama Chellappa · 2015
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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A learned representation for artistic style
Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur · 2016
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Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 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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Unsupervised domain adaptation with residual transfer networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2016
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Texture networks: Feed-forward synthesis of textures and stylized images
Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, and Victor Lempitsky · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Controlling perceptual factors in neural style transfer
Leon A Gatys, Alexander S Ecker, Matthias Bethge, Aaron Hertzmann, and Eli Shechtman · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge Belongie · 2017
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2018
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Learning to adapt structured output space for semantic segmentation
Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang, and Manmohan Chandraker · 2018
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Separating style and content for generalized style transfer
Yexun Zhang, Ya Zhang, and Wenbin Cai · 2018
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Icnet for real-time semantic segmentation on high-resolution images
Hengshuang Zhao, Xiaojuan Qi, Xiaoyong Shen, Jianping Shi, and Jiaya Jia · 2018
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Benchmarking robustness in object detection: Autonomous driving when winter is coming
Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S Ecker, Matthias Bethge, and Wieland Brendel · 2019
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Contrastive test-time adaptation
Dian Chen, Dequan Wang, Trevor Darrell, and Sayna Ebrahimi · 2022
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Stylegan-nada: Clip-guided domain adaptation of image generators
Rinon Gal, Or Patashnik, Haggai Maron, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or · 2022
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Note: Robust continual test-time adaptation against temporal correlation
Taesik Gong, Jongheon Jeong, Taewon Kim, Yewon Kim, Jinwoo Shin, and Sung-Ju Lee · 2022
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Ttn: A domain-shift aware batch normalization in test-time adaptation
Hyesu Lim, Byeonggeun Kim, Jaegul Choo, and Sungha Choi · 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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Continual test-time domain adaptation
Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai · 2022
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Yu Sun, Eric Tzeng, Trevor Darrell, and Alexei A Efros · 2019
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 · 2020
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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.
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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Dual super-resolution learning for semantic segmentation
Li Wang, Dong Li, Yousong Zhu, Lu Tian, and Yi Shan · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Evaluating prediction-time batch normalization for robustness under covariate shift, 2021
Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D’Amour, Balaji Lakshminarayanan, and Jasper Snoek · 2021
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Memo: Test time robustness via adaptation and augmentation
Marvin Zhang, Sergey Levine, and Chelsea Finn · 2022
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A probabilistic framework for lifelong test-time adaptation
Dhanajit Brahma and Piyush Rai · 2023
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Poda: Prompt-driven zero-shot domain adaptation
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, and Raoul de Charette · 2023
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Towards open-set test-time adaptation utilizing the wisdom of crowds in entropy minimization
Jungsoo Lee, Debasmit Das, 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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Label shift adapter for test-time adaptation under covariate and label shifts
Sunghyun Park, Seunghan Yang, Jaegul Choo, and Sungrack Yun · 2023
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Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization
Junha Song, Jungsoo Lee, In So Kweon, and Sungha Choi · 2023
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Dynamically instance-guided adaptation: A backward-free approach for test-time domain adaptive semantic segmentation
Wei Wang, Zhun Zhong, Weijie Wang, Xi Chen, Charles Ling, Boyu Wang, and Nicu Sebe · 2023
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