  Test-Time Adaptation (TTA) methods improve the robustness of deep neural
networks to domain shift on a variety of tasks such as image classification or
segmentation. This work explores adapting segmentation models to a single
unlabelled image with no other data available at test-time. In particular, this
work focuses on adaptation by optimizing self-supervised losses at test-time.
Multiple baselines based on different principles are evaluated under diverse
conditions and a novel adversarial training is introduced for adaptation with
mask refinement. Our additions to the baselines result in a 3.51 and 3.28 %
increase over non-adapted baselines, without these improvements, the increase
would be 1.7 and 2.16 % only.
