Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation

• URL: http://arxiv.org/abs/2412.16255v1
• Date: Fri, 20 Dec 2024 06:44:35 GMT
• Title: Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation
• Authors: Min Huang, Zifeng Xie, Bo Sun, Ning Wang,
• Abstract summary: Multi-source domain adaptation (MSDA) plays an important role in industrial model generalization. Recent efforts on MSDA focus on enhancing multi-domain distributional alignment. We propose a prototype aggregation method that models the discrepancy between source and target domains at the class and domain levels.
• Score: 8.139534851987364
• License:
• Abstract: Multi-source domain adaptation (MSDA) plays an important role in industrial model generalization. Recent efforts on MSDA focus on enhancing multi-domain distributional alignment while omitting three issues, e.g., the class-level discrepancy quantification, the unavailability of noisy pseudo-label, and source transferability discrimination, potentially resulting in suboptimal adaption performance. Therefore, we address these issues by proposing a prototype aggregation method that models the discrepancy between source and target domains at the class and domain levels. Our method achieves domain adaptation based on a group of prototypes (i.e., representative feature embeddings). A similarity score-based strategy is designed to quantify the transferability of each domain. At the class level, our method quantifies class-specific cross-domain discrepancy according to reliable target pseudo-labels. At the domain level, our method establishes distributional alignment between noisy pseudo-labeled target samples and the source domain prototypes. Therefore, adaptation at the class and domain levels establishes a complementary mechanism to obtain accurate predictions. The results on three standard benchmarks demonstrate that our method outperforms most state-of-the-art methods. In addition, we provide further elaboration of the proposed method in light of the interpretable results obtained from the analysis experiments.

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