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In this paper, we propose Test-Time Training, a general approach forimproving the performance of predictive models when training and test data comefrom different distributions. We turn a single unlabeled test sample into aself-supervised learning problem, on which we update the model parametersbefore making a prediction. This also extends naturally to data in an onlinestream. Our simple approach leads to improvements on diverse imageclassification benchmarks aimed at evaluating robustness to distributionshifts.

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