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 …

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

Test-Time Adaptation (TTA) has recently emerged as a promising approach for tackling the
robustness challenge under distribution shifts. However, the lack of consistent settings and …

When deploying a machine learning model to a new environment, we often encounter the
distribution shift problem--meaning the target data distribution is different from the model's …

Generalizing deep learning models to unknown target domain distribution with low latency
has motivated research into test-time training/adaptation (TTT/TTA). Existing approaches …

Test-time adaptation (TTA) adapts a source model to the distribution shift in testing data
without using any source data. There have been plenty of algorithms concentrated on …

Abstract Test-Time Adaptation (TTA) allows to update pre-trained models to changing data
distributions at deployment time. While early work tested these algorithms for individual fixed …

Domain adaptation (DA) techniques are frequently utilized to enhance seizure prediction
accuracy by leveraging the labeled electroencephalogram data of existing patients on new …

This article presents a comprehensive survey of online test-time adaptation (OTTA), focusing
on effectively adapting machine learning models to distributionally different target data upon …