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arXiv · 1909.13231

Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

Yu Sun·Xiaolong Wang·Zhuang Liu·John Miller·Alexei A. Efros·Moritz Hardt
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Abstract

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.

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Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt. 2019-09-29. Test-Time Training with Self-Supervision for Generalization under Distribution Shifts. https://arxiv.org/abs/1909.13231

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