Beyond model adaptation at test time: A survey

Z **ao, CGM Snoek - arxiv preprint arxiv:2411.03687, 2024 - arxiv.org
Machine learning algorithms have achieved remarkable success across various disciplines,
use cases and applications, under the prevailing assumption that training and test samples …

Navigating Distribution Shifts in Medical Image Analysis: A Survey

Z Su, J Guo, X Yang, Q Wang, F Coenen… - arxiv preprint arxiv …, 2024 - arxiv.org
Medical Image Analysis (MedIA) has become indispensable in modern healthcare,
enhancing clinical diagnostics and personalized treatment. Despite the remarkable …

Realistic Evaluation of Test-Time Adaptation Algorithms: Unsupervised Hyperparameter Selection

S Cygert, D SĂłjka, BĹ Twardowski - arxiv preprint arxiv:2407.14231, 2024 - arxiv.org
Test-Time Adaptation (TTA) has recently emerged as a promising strategy for tackling the
problem of machine learning model robustness under distribution shifts by adapting the …

Decentralizing Test-time Adaptation under Heterogeneous Data Streams

Z Su, J Guo, X Yang, Q Wang, K Huang - arxiv preprint arxiv:2411.15173, 2024 - arxiv.org
While Test-Time Adaptation (TTA) has shown promise in addressing distribution shifts
between training and testing data, its effectiveness diminishes with heterogeneous data …