Test-Time Training for Out-of-Distribution GeneralizationDownload PDF

25 Sep 2019 (modified: 24 Dec 2019)ICLR 2020 Conference Blind SubmissionReaders: Everyone
  • Original Pdf: pdf
  • TL;DR: Training on a single test input with self-supervision makes the prediction better on this input when it is out-of-distribution.
  • Abstract: We introduce a general approach, called test-time training, for improving the performance of predictive models when test and training 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 on the test sample. 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.
  • Code: https://drive.google.com/open?id=1xw-NylSnEjyHs67TXAptviOsx4YuSuZZ
  • Keywords: out-of-distribution, distribution shifts
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