On Wednesday the \(9^{\text{th}}\) of June, Henry presented Test-Time Training with Self-Supervision for Generalization under Distribution Shifts. The abstract is given below:
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.