Invariant Learning with Annotation-free Environments
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866913802949230592 |
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| author | Le, Phuong Quynh Seifert, Christin Schlötterer, Jörg |
| author_facet | Le, Phuong Quynh Seifert, Christin Schlötterer, Jörg |
| contents | Invariant learning is a promising approach to improve domain generalization compared to Empirical Risk Minimization (ERM). However, most invariant learning methods rely on the assumption that training examples are pre-partitioned into different known environments. We instead infer environments without the need for additional annotations, motivated by observations of the properties within the representation space of a trained ERM model. We show the preliminary effectiveness of our approach on the ColoredMNIST benchmark, achieving performance comparable to methods requiring explicit environment labels and on par with an annotation-free method that poses strong restrictions on the ERM reference model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15686 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Invariant Learning with Annotation-free Environments Le, Phuong Quynh Seifert, Christin Schlötterer, Jörg Machine Learning Invariant learning is a promising approach to improve domain generalization compared to Empirical Risk Minimization (ERM). However, most invariant learning methods rely on the assumption that training examples are pre-partitioned into different known environments. We instead infer environments without the need for additional annotations, motivated by observations of the properties within the representation space of a trained ERM model. We show the preliminary effectiveness of our approach on the ColoredMNIST benchmark, achieving performance comparable to methods requiring explicit environment labels and on par with an annotation-free method that poses strong restrictions on the ERM reference model. |
| title | Invariant Learning with Annotation-free Environments |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.15686 |