ICRL: independent causality representation learning for domain generalization
Abstract Domain generalization (DG) addresses the challenge of out-of-distribution (OOD) data; however, the reliance on statistical correlations during model development often introduces shortcut learning problems. Current approaches to mitigating these issues commonly involve the integration of cau...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96357-0 |
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| author | Liwen Xu Yuxuan Shao |
| author_facet | Liwen Xu Yuxuan Shao |
| author_sort | Liwen Xu |
| collection | DOAJ |
| description | Abstract Domain generalization (DG) addresses the challenge of out-of-distribution (OOD) data; however, the reliance on statistical correlations during model development often introduces shortcut learning problems. Current approaches to mitigating these issues commonly involve the integration of causal inference, formalizing DG problems through a general structural causal model. Nevertheless, ensuring the independence of features when incorporating causal models is often overlooked, leading to spurious causal relationships. In this work, we design three independent feature modules using GAN variants (GAN, WGAN, and WGAN-GP) and select the best-performing WGAN module to integrate into the existing causal model framework, constructing an independent causal relationship learning (ICRL) model. Extensive experiments on widely used datasets demonstrate that our model, with independent causal representations, outperforms the original model in both performance and efficiency, thereby validating the effectiveness of our proposed approach. The code for ICRL can be accessed at: https://github.com/22Shao/ICRL.git . |
| format | Article |
| id | doaj-art-c06cce0df7e848da8a26514ea7477ee1 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c06cce0df7e848da8a26514ea7477ee12025-08-20T03:10:11ZengNature PortfolioScientific Reports2045-23222025-04-0115111510.1038/s41598-025-96357-0ICRL: independent causality representation learning for domain generalizationLiwen Xu0Yuxuan Shao1College of Science, North China University of TechnologyCollege of Science, North China University of TechnologyAbstract Domain generalization (DG) addresses the challenge of out-of-distribution (OOD) data; however, the reliance on statistical correlations during model development often introduces shortcut learning problems. Current approaches to mitigating these issues commonly involve the integration of causal inference, formalizing DG problems through a general structural causal model. Nevertheless, ensuring the independence of features when incorporating causal models is often overlooked, leading to spurious causal relationships. In this work, we design three independent feature modules using GAN variants (GAN, WGAN, and WGAN-GP) and select the best-performing WGAN module to integrate into the existing causal model framework, constructing an independent causal relationship learning (ICRL) model. Extensive experiments on widely used datasets demonstrate that our model, with independent causal representations, outperforms the original model in both performance and efficiency, thereby validating the effectiveness of our proposed approach. The code for ICRL can be accessed at: https://github.com/22Shao/ICRL.git .https://doi.org/10.1038/s41598-025-96357-0IndependenceCausal inferenceDomain generalization |
| spellingShingle | Liwen Xu Yuxuan Shao ICRL: independent causality representation learning for domain generalization Scientific Reports Independence Causal inference Domain generalization |
| title | ICRL: independent causality representation learning for domain generalization |
| title_full | ICRL: independent causality representation learning for domain generalization |
| title_fullStr | ICRL: independent causality representation learning for domain generalization |
| title_full_unstemmed | ICRL: independent causality representation learning for domain generalization |
| title_short | ICRL: independent causality representation learning for domain generalization |
| title_sort | icrl independent causality representation learning for domain generalization |
| topic | Independence Causal inference Domain generalization |
| url | https://doi.org/10.1038/s41598-025-96357-0 |
| work_keys_str_mv | AT liwenxu icrlindependentcausalityrepresentationlearningfordomaingeneralization AT yuxuanshao icrlindependentcausalityrepresentationlearningfordomaingeneralization |