Fetching the paper…
Reading the bibliography…
Test-Time Adaptation (TTA) aims to adapt pre-trained models to the target domain during testing.
Stochastic processes
Sheldon M Ross · 1995
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 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.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
Earlier work this paper cites.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Earlier work this paper cites.
Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell · 2018
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.
Universal source-free domain adaptation
Jogendra Nath Kundu, Naveen Venkat, R Venkatesh Babu, et al · 2020
Earlier work this paper cites.
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
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.
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.
A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 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.
Dataset shift in machine learning
Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence · 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.
Learning instance-specific adaptation for cross-domain segmentation
Yuliang Zou, Zizhao Zhang, Chun-Liang Li, Han Zhang, Tomas Pfister, and Jia-Bin Huang · 2022
Later among the works it cites.
A probabilistic framework for lifelong test-time adaptation
Dhanajit Brahma and Piyush Rai · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Test-time classifier adjustment module for model-agnostic domain generalization
Yusuke Iwasawa and Yutaka Matsuo · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Cited alongside, same era.
Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
Cited alongside, same era.
Test-time adaptation with shape moments for image segmentation
Mathilde Bateson, Herve Lombaert, and Ismail Ben Ayed · 2022
Cited alongside, same era.
Parameter-free online test-time adaptation
Malik Boudiaf, Romain Mueller, Ismail Ben Ayed, and Luca Bertinetto · 2022
Cited alongside, same era.
Test-time training with masked autoencoders
Yossi Gandelsman, Yu Sun, Xinlei Chen, and Alexei Efros · 2022
Cited alongside, same era.
Note: Robust continual test-time adaptation against temporal correlation
Taesik Gong, Jongheon Jeong, Taewon Kim, Yewon Kim, Jinwoo Shin, and Sung-Ju Lee · 2022
Cited alongside, same era.
Robust test-time adaptation in dynamic scenarios
Longhui Yuan, Binhui Xie, and Shuang Li · 2023
Later among the works it cites.
ODS: Test-time adaptation in the presence of open-world data shift
Zhi Zhou, Lan-Zhe Guo, Lin-Han Jia, Dingchu Zhang, and Yu-Feng Li · 2023
Later among the works it cites.
SimPro: A simple probabilistic framework towards realistic long-tailed semi-supervised learning
Chaoqun Du, Yizeng Han, and Gao Huang · 2024
Closest in time.
Probabilistic contrastive learning for long-tailed visual recognition
Chaoqun Du, Yulin Wang, Shiji Song, and Gao Huang · 2024
Closest in time.
Everything to the synthetic: Diffusion-driven test-time adaptation via synthetic-domain alignment
Jiayi Guo, Junhao Zhao, Chunjiang Ge, Chaoqun Du, Zanlin Ni, Shiji Song, Humphrey Shi, and Gao Huang · 2024
Closest in time.
Universal test-time adaptation through weight ensembling, diversity weighting, and prior correction
Robert A Marsden, Mario Döbler, and Bin Yang · 2024
Closest in time.
Towards real-world test-time adaptation: Tri-net self-training with balanced normalization
Yongyi Su, Xun Xu, and Kui Jia · 2024
Closest in time.
Un-mixing test-time normalization statistics: Combatting label temporal correlation
Devavrat Tomar, Guillaume Vray, Jean-Philippe Thiran, and Behzad Bozorgtabar · 2024
Closest in time.