Introduction
Machine learning models often rely on "spurious correlations"—shortcuts in data that do not generalize to the real world. For instance, a model might identify malignant lesions by the presence of a ruler in the image rather than the lesion's characteristics. While Invariant Risk Minimization (IRM) aims to find features that remain predictive across different "environments," it typically requires expensive, manually labeled environment annotations. This paper introduces a method to automatically discover these environments by clustering internal neural network representations, achieving performance comparable to human-annotated methods.
Methodology: Automated Environment Discovery
The proposed pipeline removes the need for manual annotations through a multi-step process:
- Reference Model Training: A standard Empirical Risk Minimization (ERM) model is trained. Since ERM naturally picks up shortcuts, its penultimate layer's representation space organizes around these spurious features.
- Clustering: Embeddings from the reference model are grouped using k-means clustering.
- Conflict Sample Identification: Within each cluster, "minority cases" (samples that share spurious features but have different class labels) are identified.
- Environment Construction: Two training sets are created: a Minority Set ($D_m$) of conflict samples and a Balanced Set ($D_{balance}$).
- Invariant Training: An IRM model is trained using these inferred environments instead of human-labeled ones.
Experimental Results
The method was evaluated on the ColoredMNIST benchmark, where digit color is 90% correlated with the label in training but reversed in testing. A model relying on color will fail on the test set.
| Method | Test Accuracy (%) |
|---|---|
| Standard ERM | 16.9 |
| Standard IRM (Oracle) | 66.9 |
| Proposed Method | 68.0 |
| EIIL (State-of-the-art) | 68.4 |
The proposed method slightly outperformed the "oracle" IRM that uses human-labeled environments, demonstrating that automated clustering can capture spurious structures more effectively than manual labeling in some cases.
Key Insights
- Shortcut Purity: Internal clusters are often more consistent regarding spurious features (like color) than actual class labels, making them ideal for environment discovery.
- The Power of Minority Samples: By specifically isolating samples that "break the rule" (conflict samples), the model is forced to ignore the shortcut. If the model sees environments where the same color corresponds to different labels, it must focus on the actual shape of the digit.
- Zero Overhead: Unlike other annotation-free methods that require complex optimization or specific early-stopping criteria, this method uses a standard trained model and basic clustering.
Conclusion and Future Work
While successful on synthetic benchmarks like ColoredMNIST, the authors acknowledge that real-world data often contains multiple, overlapping spurious correlations. Future work will focus on scaling the method to more complex datasets and developing automated ways to select hyperparameters, such as the optimal number of clusters.