  An unresolved problem in Deep Learning is the ability of neural networks to
cope with domain shifts during test-time, imposed by commonly fixing network
parameters after training. Our proposed method Meta Test-Time Training (MT3),
however, breaks this paradigm and enables adaption at test-time. We combine
meta-learning, self-supervision and test-time training to learn to adapt to
unseen test distributions. By minimizing the self-supervised loss, we learn
task-specific model parameters for different tasks. A meta-model is optimized
such that its adaption to the different task-specific models leads to higher
performance on those tasks. During test-time a single unlabeled image is
sufficient to adapt the meta-model parameters. This is achieved by minimizing
only the self-supervised loss component resulting in a better prediction for
that image. Our approach significantly improves the state-of-the-art results on
the CIFAR-10-Corrupted image classification benchmark. Our implementation is
available on GitHub.
