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 …

Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

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 …

This paper presents a simple yet effective approach that improves continual test-time
adaptation (TTA) in a memory-efficient manner. TTA may primarily be conducted on edge …

The main challenge in domain generalization (DG) is to handle the distribution shift problem
that lies between the training and test data. Recent studies suggest that test-time training …

Abstract Deep Neural Networks have exhibited considerable success in various visual tasks.
However, when applied to unseen test datasets, state-of-the-art models often suffer …

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) methods, which generally rely on the model's predictions (eg,
entropy minimization) to adapt the source pretrained model to the unlabeled target domain …

Single domain generalization aims to train a generalizable model with only one source
domain to perform well on arbitrary unseen target domains. Image augmentation based on …

Test-time adaptation (TTA) aims to adapt a pre-trained model to the target domain in a batch-
by-batch manner during inference. While label distributions often exhibit imbalances in real …