Less is More: Pseudo-Label Filtering for Continual Test-Time Adaptation

• URL: http://arxiv.org/abs/2406.02609v2
• Date: Fri, 12 Jul 2024 08:15:22 GMT
• Title: Less is More: Pseudo-Label Filtering for Continual Test-Time Adaptation
• Authors: Jiayao Tan, Fan Lyu, Chenggong Ni, Tingliang Feng, Fuyuan Hu, Zhang Zhang, Shaochuang Zhao, Liang Wang,
• Abstract summary: 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. Existing methods rely on constructing pseudo-labels for all samples and updating the model through self-training. We propose Pseudo Labeling Filter (PLF) to improve the quality of pseudo-labels.
• Score: 13.486951040331899
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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