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Beyond model adaptation at test time: A survey
Machine learning algorithms have achieved remarkable success across various disciplines,
use cases and applications, under the prevailing assumption that training and test samples …
use cases and applications, under the prevailing assumption that training and test samples …
Navigating Distribution Shifts in Medical Image Analysis: A Survey
Medical Image Analysis (MedIA) has become indispensable in modern healthcare,
enhancing clinical diagnostics and personalized treatment. Despite the remarkable …
enhancing clinical diagnostics and personalized treatment. Despite the remarkable …
Realistic Evaluation of Test-Time Adaptation Algorithms: Unsupervised Hyperparameter Selection
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 …
problem of machine learning model robustness under distribution shifts by adapting the …
Decentralizing Test-time Adaptation under Heterogeneous Data Streams
While Test-Time Adaptation (TTA) has shown promise in addressing distribution shifts
between training and testing data, its effectiveness diminishes with heterogeneous data …
between training and testing data, its effectiveness diminishes with heterogeneous data …