Invariant Learning with Annotation-free Environments

2025-04-22

AI Summary

This work addresses the bottleneck in domain generalization caused by reliance on manually annotated environment labels. We propose a novel paradigm that automatically infers heterogeneous environments without requiring additional environment annotations. Our core method is the first to exploit the intrinsic geometric structure of the representation space learned by an empirical risk minimization (ERM) model; it performs unsupervised geometric analysis to implicitly identify environment partitions and integrates them into the invariant risk minimization (IRM) framework. This approach eliminates dependence on explicit environment labels and strong modeling assumptions. On the ColoredMNIST benchmark, our method achieves generalization performance comparable to supervised IRM methods using ground-truth environment labels—despite leveraging only the internal structural properties of the training data—and significantly outperforms existing unsupervised domain generalization approaches. The proposed framework offers a scalable, low-overhead solution for robust generalization in environment-agnostic settings.

Technology Category

• Machine Learning: Learning with Manifolds
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Representation Learning for Vision

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• Improves domain generalization via invariant learning
• Infers environments without annotation requirements
• Validates effectiveness on ColoredMNIST benchmark

Innovation

• Annotation-free environment inference method
• Leverages ERM model representation space
• Competes with labeled environment methods