  Test time adaptation (TTA) aims to adapt deep neural networks when receiving
out of distribution test domain samples. In this setting, the model can only
access online unlabeled test samples and pre-trained models on the training
domains. We first address TTA as a feature revision problem due to the domain
gap between source domains and target domains. After that, we follow the two
measurements alignment and uniformity to discuss the test time feature
revision. For test time feature uniformity, we propose a test time
self-distillation strategy to guarantee the consistency of uniformity between
representations of the current batch and all the previous batches. For test
time feature alignment, we propose a memorized spatial local clustering
strategy to align the representations among the neighborhood samples for the
upcoming batch. To deal with the common noisy label problem, we propound the
entropy and consistency filters to select and drop the possible noisy labels.
To prove the scalability and efficacy of our method, we conduct experiments on
four domain generalization benchmarks and four medical image segmentation tasks
with various backbones. Experiment results show that our method not only
improves baseline stably but also outperforms existing state-of-the-art test
time adaptation methods. Code is available at
\href{https://github.com/SakurajimaMaiii/TSD}{https://github.com/SakurajimaMaiii/TSD}.
