  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.
