Variational Continual Test-Time Adaptation
Fan Lyu Kaile Du Yuyang Li Hanyu Zhao Zhang Zhang Guangcan Liu Liang Wang
The prior drift is crucial in Continual Test-Time Adaptation (CTTA) methods
that only use unlabeled test data, as it can cause significant error
propagation. In this paper, we introduce VCoTTA, a variational Bayesian
approach to measure uncertainties in CTTA. At the source stage, we transform a
pre-trained deterministic model into a Bayesian Neural Network (BNN) via a
variational warm-up strategy, injecting uncertainties into the model. During
the testing time, we employ a mean-teacher update strategy using variational
inference for the student model and exponential moving average for the teacher
model. Our novel approach updates the student model by combining priors from
both the source and teacher models. The evidence lower bound is formulated as
the cross-entropy between the student and teacher models, along with the
Kullback-Leibler (KL) divergence of the prior mixture. Experimental results on
three datasets demonstrate the method's effectiveness in mitigating prior drift
within the CTTA framework.