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

Code

• github.com/yueatsprograms/ttt_cifar_release
  Officialpytorch★ 0
• github.com/yueatsprograms/ttt_imagenet_release
  Officialpytorch★ 0
• github.com/tejas-gokhale/AGAT
  pytorch★ 4

Abstract

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.

Tasks

Benchmark Results

Reproductions