No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems (2011.12945v2)

Abstract: In real-world classification tasks, each class often comprises multiple finer-grained "subclasses." As the subclass labels are frequently unavailable, models trained using only the coarser-grained class labels often exhibit highly variable performance across different subclasses. This phenomenon, known as hidden stratification, has important consequences for models deployed in safety-critical applications such as medicine. We propose GEORGE, a method to both measure and mitigate hidden stratification even when subclass labels are unknown. We first observe that unlabeled subclasses are often separable in the feature space of deep neural networks, and exploit this fact to estimate subclass labels for the training data via clustering techniques. We then use these approximate subclass labels as a form of noisy supervision in a distributionally robust optimization objective. We theoretically characterize the performance of GEORGE in terms of the worst-case generalization error across any subclass. We empirically validate GEORGE on a mix of real-world and benchmark image classification datasets, and show that our approach boosts worst-case subclass accuracy by up to 22 percentage points compared to standard training techniques, without requiring any prior information about the subclasses.

• The paper introduces a novel framework that improves fine-grained robustness in coarse-grained tasks by uncovering hidden subclass structures.
• The paper leverages unsupervised clustering on neural network features to estimate subclass labels which are then used in robust optimization.
• Empirical results on datasets such as ISIC demonstrate significantly boosted worst-case accuracy, highlighting practical benefits for critical applications.

Fine-Grained Robustness in Coarse-Grained Classification Problems

The paper introduces a novel approach to tackling the unique challenge of hidden stratification in coarse-grained classification problems, where the goal is to improve model performance across unmarked finer-grained subclasses of labeled superclasses. This issue becomes particularly pertinent in critical applications like medical diagnosis, where overlooking specific subclasses can lead to significant risks. The paper proposes "No Subclass Left Behind" (), a framework designed to achieve fine-grained robustness even without explicit subclass labels.

Summary of Contributions

The authors present a clear framework that addresses hidden stratification in two main steps:

1. Estimation of Subclass Labels: The method leverages the feature space of neural network models to separate subclasses through clustering techniques. By observing that even unlabeled subclasses naturally segregate within the feature space, the framework estimates subclass labels using unsupervised clustering approaches.
2. Robust Optimization: After estimating subclass labels, these are used within a distributionally robust optimization framework to train a classifier optimized for worst-case performance across these subclasses.

The paper also discusses the theoretical underpinnings of their method, showing that under certain conditions, it achieves sample complexity comparable to knowing true subclass labels. Through empirical validation across several datasets, including real-world ones like ISIC for skin lesions and medical image classification, significantly boosted worst-case subclass accuracy is demonstrated.

Implications and Future Prospects

The implications of this work are multifaceted:

• Practical Impact: For industries that heavily rely on machine learning models for critical classification tasks, this framework offers a way to ensure balanced performance across various, often undocumented, subclasses without the need for exhaustive labeling efforts.
• Theoretical Insight: The analyses provide insight into how latent subclass structures can be uncovered and exploited to achieve robustness, setting a foundation for further exploration into subclass prediction and optimization domains.

Looking forward, the methodology invites several lines of exploration, notably in improving the efficiency and scalability of subclass estimation in very large datasets or exploring the integration of other unsupervised learning techniques. Additionally, the utilization of pretrained embeddings, as evidenced by the significant improvements when applied to CelebA dataset, suggests a promising avenue for leveraging transfer learning in other extensive classification tasks.

Conclusion

This paper presents a compelling approach to overcoming limitations in traditional classification models by focusing on robustness across finer-grained details within broader classes. The intricate balance between practical implementation and theoretical development broadens the application of robust machine learning models, emphasizing the importance of considering all possible subdivisions within a dataset to ensure complete reliability and performance equity.