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) addresses the unforeseen distribution shifts occurring during test
time. In TTA, performance, memory consumption, and time consumption are crucial …

The advancements in generative modeling, particularly the advent of diffusion models, have
sparked a fundamental question: how can these models be effectively used for …

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 …

Test-Time Adaptation aims to adapt source domain model to testing data at inference stage
with success demonstrated in adapting to unseen corruptions. However, these attempts may …

Personalized federated learning algorithms have shown promising results in adapting
models to various distribution shifts. However, most of these methods require labeled data …

Test-time adaptation (TTA) aims to address the distribution shift between the training and
test data with only unlabeled data at test time. Existing TTA methods often focus on …

Test-time adaptation (TTA) has emerged as a promising approach to dealing with latent
distribution shifts between training and testing data. However, most of existing TTA methods …

Test-time adaptation (TTA) is a technique aimed at enhancing the generalization
performance of models by leveraging unlabeled samples solely during prediction. Given the …

Test-time adaptation (TTA) addresses distribution shifts for streaming test data in
unsupervised settings. Currently, most TTA methods can only deal with minor shifts and rely …