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Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
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<span title="2020-07-01">2020</span>
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arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2019-09-29">2019</span>
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<span title="2019-10-25">2019</span>
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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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