Text-Driven Causal Representation Learning for Source-Free Domain Generalization

• URL: http://arxiv.org/abs/2507.09961v1
• Date: Mon, 14 Jul 2025 06:20:42 GMT
• Title: Text-Driven Causal Representation Learning for Source-Free Domain Generalization
• Authors: Lihua Zhou, Mao Ye, Nianxin Li, Shuaifeng Li, Jinlin Wu, Xiatian Zhu, Lei Deng, Hongbin Liu, Jiebo Luo, Zhen Lei,
• Abstract summary: We propose TDCRL, the first method to integrate causal inference into the source-free domain generalization setting.<n>Our approach offers a clear and effective mechanism to achieve robust, domain-invariant features, ensuring robust generalization.
• Score: 82.75041792888274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning often struggles when training and test data distributions differ. Traditional domain generalization (DG) tackles this by including data from multiple source domains, which is impractical due to expensive data collection and annotation. Recent vision-language models like CLIP enable source-free domain generalization (SFDG) by using text prompts to simulate visual representations, reducing data demands. However, existing SFDG methods struggle with domain-specific confounders, limiting their generalization capabilities. To address this issue, we propose TDCRL (\textbf{T}ext-\textbf{D}riven \textbf{C}ausal \textbf{R}epresentation \textbf{L}earning), the first method to integrate causal inference into the SFDG setting. TDCRL operates in two steps: first, it employs data augmentation to generate style word vectors, combining them with class information to generate text embeddings to simulate visual representations; second, it trains a causal intervention network with a confounder dictionary to extract domain-invariant features. Grounded in causal learning, our approach offers a clear and effective mechanism to achieve robust, domain-invariant features, ensuring robust generalization. Extensive experiments on PACS, VLCS, OfficeHome, and DomainNet show state-of-the-art performance, proving TDCRL effectiveness in SFDG.

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