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

MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption