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 …

Since real-world machine systems are running in non-stationary environments, Continual
Test-Time Adaptation (CTTA) task is proposed to adapt the pre-trained model to continually …

Since distribution shifts are likely to occur during test-time and can drastically decrease the
model's performance, online test-time adaptation (TTA) continues to update the model after …

Distribution shift widely exists in medical images acquired from different medical centres and
poses a significant obstacle to deploying the pre-trained semantic segmentation model in …

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 …

Existing domain adaptation (DA) methods typically require access to source domain data,
which raises privacy concerns due to the sensitive information contained in …

Real-world systems often encounter new data over time which leads to experiencing target
domain shifts. Existing Test-Time Adaptation (TTA) methods tend to apply computationally …

Given the inevitability of domain shifts during inference in real-world applications, test-time
adaptation (TTA) is essential for model adaptation after deployment. However, the real-world …

Given a model trained on source data, Test-Time Adaptation (TTA) enables adaptation and
inference in test data streams with domain shifts from the source. Current methods …