Refining the Information Bottleneck via Adversarial Information Separation
This addresses the challenge of generalizing from limited data in domains such as material science, where noise confounds features, representing an incremental improvement over existing adversarial methods.
The paper tackles the problem of separating task-relevant features from noise in data-scarce domains like material science, proposing the Adversarial Information Separation Framework (AdverISF) which outperforms state-of-the-art methods and achieves superior generalization in real-world tasks.
Generalizing from limited data is particularly critical for models in domains such as material science, where task-relevant features in experimental datasets are often heavily confounded by measurement noise and experimental artifacts. Standard regularization techniques fail to precisely separate meaningful features from noise, while existing adversarial adaptation methods are limited by their reliance on explicit separation labels. To address this challenge, we propose the Adversarial Information Separation Framework (AdverISF), which isolates task-relevant features from noise without requiring explicit supervision. AdverISF introduces a self-supervised adversarial mechanism to enforce statistical independence between task-relevant features and noise representations. It further employs a multi-layer separation architecture that progressively recycles noise information across feature hierarchies to recover features inadvertently discarded as noise, thereby enabling finer-grained feature extraction. Extensive experiments demonstrate that AdverISF outperforms state-of-the-art methods in data-scarce scenarios. In addition, evaluations on real-world material design tasks show that it achieves superior generalization performance.