Fetching the paper…
Reading the bibliography…
Fully-test-time adaptation (F-TTA) can mitigate performance loss due to distribution shifts between train and test data (1) without access to the training data, and (2) without knowledge of the model training procedure.
A simple method for robust regression
Melvin J Hinich and Prem P Talwar · 1975
Earlier work this paper cites.
Techniques for nonlinear least squares and robust regression
John E Dennis Jr and Roy E Welsch · 1978
Earlier work this paper cites.
Robust estimation of a location parameter
Peter J Huber · 1992
Earlier work this paper cites.
On the unification line processes, outlier rejection, and robust statistics with applications in early vision
Michael Black and Anand Rangarajan · 1996
Earlier work this paper cites.
The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields
Michael J Black and Paul Anandan · 1996
Earlier work this paper cites.
Dragon systems’ 1998 broadcast news transcription system
Steven Wegmann, Puming Zhan, Ira Carp, Michael Newman, Jon Yamron, and Larry Gillick · 1999
Earlier work this paper cites.
Self-paced learning for latent variable models
M Kumar, Benjamin Packer, and Daphne Koller · 2010
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.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Earlier work this paper cites.
Revisiting batch normalization for practical domain adaptation
Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, and Xiaodi Hou · 2016
Earlier work this paper cites.
Self-paced learning: An implicit regularization perspective
Yanbo Fan, Ran He, Jian Liang, and Baogang Hu · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Earlier work this paper cites.
Group normalization
Yuxin Wu and Kaiming He · 2018
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Earlier work this paper cites.
A general and adaptive robust loss function
Jonathan T Barron · 2019
Earlier work this paper cites.
Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
Earlier work this paper cites.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Earlier work this paper cites.
A review of domain adaptation without target labels
Wouter M Kouw and Marco Loog · 2019
Earlier work this paper cites.
Xinyu Zhang, Qiang Wang, Jian Zhang, and Zhao Zhong · 2019
Cited alongside, same era.
One-shot unsupervised cross-domain detection
Antonio D’Innocente, Francesco Cappio Borlino, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi · 2020
Cited alongside, same era.
Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
Cited alongside, same era.
Universal source-free domain adaptation
Jogendra Nath Kundu, Naveen Venkat, R Venkatesh Babu, et al · 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.
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
Later among the works it cites.
Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
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.
Improving test-time adaptation via shift-agnostic weight regularization and nearest source prototypes
Sungha Choi, Seunghan Yang, Seokeon Choi, and Sungrack Yun · 2022
Later among the works it cites.
Variational model perturbation for source-free domain adaptation
Mengmeng Jing, Xiantong Zhen, Jingjing Li, and Cees Snoek · 2022
Later among the works it cites.
Multi-step test-time adaptation with entropy minimization and pseudo-labeling
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jian Liang, Dapeng 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.
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.
Choosing the sample with lowest loss makes sgd robust
Vatsal Shah, Xiaoxia Wu, and Sujay Sanghavi · 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.
Revisiting batch normalization for improving corruption robustness
Philipp Benz, Chaoning Zhang, Adil Karjauv, and In So Kweon · 2021
Cited alongside, same era.
A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars · 2021
Cited alongside, same era.
Hiroaki Kingetsu, Kenichi Kobayashi, Yoshihiro Okawa, Yasuto Yokota, and Katsuhito Nakazawa · 2022
Later among the works it cites.
Surgical fine-tuning improves adaptation to distribution shifts
Yoonho Lee, Annie S Chen, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, and Chelsea Finn · 2022
Later among the works it cites.
Guan-Ting Lin, Shang-Wen Li, and Hung-yi Lee · 2022
Later among the works it cites.
Efficient test-time model adaptation without forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan · 2022
Later among the works it cites.
Continual test-time domain adaptation
Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai · 2022
Later among the works it cites.
Memo: Test time robustness via adaptation and augmentation
Marvin Zhang, Sergey Levine, and Chelsea Finn · 2022
Later among the works it cites.
Domain generalization: A survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy · 2022
Later among the works it cites.
Back to the source: Diffusion-driven adaptation to test-time corruption
Jin Gao, Jialing Zhang, Xihui Liu, Trevor Darrell, Evan Shelhamer, and Dequan Wang · 2023
Closest in time.
Changhun Kim, Joonhyung Park, Hajin Shim, and Eunho Yang · 2023
Closest in time.
A comprehensive survey on test-time adaptation under distribution shifts
Jian Liang, Ran He, and Tieniu Tan · 2023
Closest in time.
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
Closest in time.
Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization
Junha Song, Jungsoo Lee, In So Kweon, and Sungha Choi · 2023
Closest in time.
Auto: Adaptive outlier optimization for online test-time ood detection
Puning Yang, Jian Liang, Jie Cao, and Ran He · 2023
Closest in time.
On pitfalls of test-time adaptation
Hao Zhao, Yuejiang Liu, Alexandre Alahi, and Tao Lin · 2023
Closest in time.