Invariant Learning with Annotation-free Environments

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.

@article{le2025_2504.15686,
  title={ Invariant Learning with Annotation-free Environments },
  author={ Phuong Quynh Le and Christin Seifert and Jörg Schlötterer },
  journal={arXiv preprint arXiv:2504.15686},
  year={ 2025 }
}