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Poster
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
Yu Sun · Xiaolong Wang · Zhuang Liu · John Miller · Alexei Efros · Moritz Hardt

Wed Jul 15 10:00 AM -- 10:45 AM & Wed Jul 15 11:00 PM -- 11:45 PM (PDT) @ Virtual
in Poster Session 24 »

In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions. We turn a single unlabeled test sample into a self-supervised learning problem, on which we update the model parameters before making a prediction. This also extends naturally to data in an online stream. Our simple approach leads to improvements on diverse image classification benchmarks aimed at evaluating robustness to distribution shifts.

Keywords: Algorithms · Robust Statistics and Machine Learning · Trustworthy Machine Learning
[ Slides] 

Author Information

Yu Sun (UC Berkeley)
Xiaolong Wang (UC Berkeley)
Zhuang Liu (UC Berkeley)
John Miller (University of California, Berkeley)
Alexei Efros (UC Berkeley)
Moritz Hardt (University of California, Berkeley)

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