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A comprehensive survey on test-time adaptation under distribution shifts
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 …
process that can effectively generalize to test samples, even in the presence of distribution …
The entropy enigma: Success and failure of entropy minimization
O Press, R Shwartz-Ziv, Y LeCun, M Bethge - ar** the online data buffering and organizing mechanism for continual test-time adaptation
Abstract Continual Test-Time Adaptation (CTTA) involves adapting a pre-trained source
model to continually changing unsupervised target domains. In this paper, we systematically …
model to continually changing unsupervised target domains. In this paper, we systematically …
AETTA: Label-Free Accuracy Estimation for Test-Time Adaptation
Test-time adaptation (TTA) has emerged as a viable solution to adapt pre-trained models to
domain shifts using unlabeled test data. However TTA faces challenges of adaptation …
domain shifts using unlabeled test data. However TTA faces challenges of adaptation …
STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay
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 data with only unlabeled data at test time. Existing TTA methods often focus on …
Investigating continual pretraining in large language models: Insights and implications
Ç Yıldız, NK Ravichandran, P Punia, M Bethge… - ar** strategies for efficient and sustainable training …
Stationary latent weight inference for unreliable observations from online test-time adaptation
In the rapidly evolving field of online test-time adaptation (OTTA), effectively managing
distribution shifts is a pivotal concern. State-of-the-art OTTA methodologies often face …
distribution shifts is a pivotal concern. State-of-the-art OTTA methodologies often face …
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 …
Santa: Source anchoring network and target alignment for continual test time adaptation
Adapting a trained model to perform satisfactorily on continually changing test environments
is an important and challenging task. In this work, we propose a novel framework, SANTA …
is an important and challenging task. In this work, we propose a novel framework, SANTA …