Continual Test-Time Adaptation (CTTA) aims to adapt a pre-trained model to a
sequence of target domains during the test phase without accessing the source
data. To adapt to unlabeled data from unknown domains, existing methods rely on
constructing pseudo-labels for all samples and updating the model through
self-training. However, these pseudo-labels often involve noise, leading to
insufficient adaptation. To improve the quality of pseudo-labels, we propose a
pseudo-label selection method for CTTA, called Pseudo Labeling Filter (PLF).
The key idea of PLF is to keep selecting appropriate thresholds for
pseudo-labels and identify reliable ones for self-training. Specifically, we
present three principles for setting thresholds during continuous domain
learning, including initialization, growth and diversity. Based on these
principles, we design Self-Adaptive Thresholding to filter pseudo-labels.
Additionally, we introduce a Class Prior Alignment (CPA) method to encourage
the model to make diverse predictions for unknown domain samples. Through
extensive experiments, PLF outperforms current state-of-the-art methods,
proving its effectiveness in CTTA.

在测试阶段无需访问源数据，连续测试时适应 (CTTA) 旨在将预训练模型调整到一系列目标领域。为了适应未知领域的无标签数据，现有方法依赖于为所有样本构建伪标签并通过自我训练来更新模型。然而，这些伪标签往往包含噪声，导致适应不足。为了提高伪标签的质量，我们提出了一种 CTTA 的伪标签选择方法，称为 Pseudo Labeling Filter (PLF)。PLF 的关键思想是为伪标签持续选择适当的阈值，并识别可靠的伪标签用于自我训练。具体而言，我们提出了连续领域学习中设置阈值的三个原则，包括初始化、增长和多样性。基于这些原则，我们设计出了自适应阈值方法来过滤伪标签。此外，我们引入了一种类先验对齐方法来鼓励模型对未知领域样本进行多样化预测。通过大量实验证明，PLF 优于当前最先进的方法，证明了其在 CTTA 中的有效性。