Text-Driven Causal Representation Learning for Source-Free Domain Generalization
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912481127956480 |
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| author | Zhou, Lihua Ye, Mao Li, Nianxin Li, Shuaifeng Wu, Jinlin Zhu, Xiatian Deng, Lei Liu, Hongbin Luo, Jiebo Lei, Zhen |
| author_facet | Zhou, Lihua Ye, Mao Li, Nianxin Li, Shuaifeng Wu, Jinlin Zhu, Xiatian Deng, Lei Liu, Hongbin Luo, Jiebo Lei, Zhen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09961 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Text-Driven Causal Representation Learning for Source-Free Domain Generalization Zhou, Lihua Ye, Mao Li, Nianxin Li, Shuaifeng Wu, Jinlin Zhu, Xiatian Deng, Lei Liu, Hongbin Luo, Jiebo Lei, Zhen Machine Learning 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. |
| title | Text-Driven Causal Representation Learning for Source-Free Domain Generalization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.09961 |