Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation

arXiv:2312.12480v2 Announce Type: replace
Abstract: Continual Test-Time Adaptation (CTTA) is proposed to migrate a source pre-trained model to continually changing target distributions, addressing real-world dynamism. Existing CTTA methods mainly rely on entropy minimization or teacher-student pseudo-labeling schemes for knowledge extraction in unlabeled target domains. However, dynamic data distributions cause miscalibrated predictions and noisy pseudo-labels in existing self-supervised learning methods, hindering the effective mitigation of error accumulation and catastrophic forgetting problems during the continual adaptation process. To tackle these issues, we …

arxiv autoencoders continual cs.cv distribution test type