Test-Time Training for Generalization under Distribution Shifts

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Authors

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

Abstract

<p>We introduce a general approach, called test-time training, for improving the performance of predictive models when training and test data come from different distributions. Test-time training turns a single unlabeled test instance into a self-supervised learning problem, on which we update the model parameters before making a prediction. We show that this simple idea leads to surprising improvements on diverse image classification benchmarks aimed at evaluating robustness to distribution shifts. Theoretical investigations on a convex model reveal helpful intuitions for when we can expect our approach to help.</p>