CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization

• URL: http://arxiv.org/abs/2412.08479v1
• Date: Wed, 11 Dec 2024 15:47:01 GMT
• Title: CAT: Class Aware Adaptive Thresholding for Semi-Supervised Domain Generalization
• Authors: Sumaiya Zoha, Jeong-Gun Lee, Young-Woong Ko,
• Abstract summary: Domain Generalization seeks to transfer knowledge from source domains to unseen target domains, even in the presence of domain shifts. We propose a novel method, CAT, which leverages semi-supervised learning with limited labeled data to achieve competitive generalization performance under domain shifts. Our approach uses flexible thresholding to generate high-quality pseudo-labels with higher class diversity while refining noisy pseudo-labels to improve their reliability.
• Score: 0.989976359821412
• License:
• Abstract: Domain Generalization (DG) seeks to transfer knowledge from multiple source domains to unseen target domains, even in the presence of domain shifts. Achieving effective generalization typically requires a large and diverse set of labeled source data to learn robust representations that can generalize to new, unseen domains. However, obtaining such high-quality labeled data is often costly and labor-intensive, limiting the practical applicability of DG. To address this, we investigate a more practical and challenging problem: semi-supervised domain generalization (SSDG) under a label-efficient paradigm. In this paper, we propose a novel method, CAT, which leverages semi-supervised learning with limited labeled data to achieve competitive generalization performance under domain shifts. Our method addresses key limitations of previous approaches, such as reliance on fixed thresholds and sensitivity to noisy pseudo-labels. CAT combines adaptive thresholding with noisy label refinement techniques, creating a straightforward yet highly effective solution for SSDG tasks. Specifically, our approach uses flexible thresholding to generate high-quality pseudo-labels with higher class diversity while refining noisy pseudo-labels to improve their reliability. Extensive experiments across multiple benchmark datasets demonstrate the superior performance of our method, highlighting its effectiveness in achieving robust generalization under domain shift.

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