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.

翻译：在仅使用无标签测试数据的持续测试时自适应（CTTA）方法中，先验漂移至关重要，因为它可能导致显著的误差传播。本文提出VCoTTA，一种基于变分贝叶斯的方法，用于量化CTTA中的不确定性。在源域阶段，我们通过变分预热策略将预训练的确定性模型转化为贝叶斯神经网络（BNN），从而向模型中注入不确定性。在测试阶段，我们采用均值教师更新策略：对学生模型使用变分推理，对教师模型使用指数移动平均。这一创新方法通过结合源模型和教师模型的先验来更新学生模型。证据下界被公式化为学生模型与教师模型之间的交叉熵，以及先验混合的KL散度。在三个数据集上的实验结果表明，该方法能有效缓解CTTA框架中的先验漂移问题。