  Nowadays, deep neural networks outperform humans in many tasks. However, if
the input distribution drifts away from the one used in training, their
performance drops significantly. Recently published research has shown that
adapting the model parameters to the test sample can mitigate this performance
degradation. In this paper, we therefore propose a novel modification of the
self-supervised training algorithm SwAV that adds the ability to adapt to
single test samples. Using the provided prototypes of SwAV and our derived
test-time loss, we align the representation of unseen test samples with the
self-supervised learned prototypes. We show the success of our method on the
common benchmark dataset CIFAR10-C.
