Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

Test-time adaptation (TTA) intends to adapt the pretrained model to test distributions with
only unlabeled test data streams. Most of the previous TTA methods have achieved great …

Test-time adaptation (TTA) is an emerging paradigm that addresses distributional shifts
between training and testing phases without additional data acquisition or labeling cost; only …

Test-time adaptation (TTA) refers to adapting neural networks to distribution shifts,
specifically with just access to unlabeled test samples from the new domain at test-time …

Test-time adaptation (TTA) is a special and practical setting in unsupervised domain
adaptation, which allows a pre-trained model in a source domain to adapt to unlabeled test …

Abstract Continual Test-Time Adaptation (CTTA) aims to adapt the source model to
continually changing unlabeled target domains without access to the source data. Existing …

Test-time adaptation (TTA) aims to address distributional shifts between training and testing
data using only unlabeled test data streams for continual model adaptation. However, most …

This paper proposes a novel batch normalization strategy for test-time adaptation. Recent
test-time adaptation methods heavily rely on the modified batch normalization, ie …

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 …