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arXiv · 2504.15686

Invariant Learning with Annotation-free Environments

Phuong Quynh Le·Christin Seifert·Jörg Schlötterer
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Abstract

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.

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Phuong Quynh Le, Christin Seifert, Jörg Schlötterer. 2025-04-22. Invariant Learning with Annotation-free Environments. https://arxiv.org/abs/2504.15686

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