Test-Time Training for Generalization under Distribution Shifts

Abstract

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.

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Reproductions