Refining the Information Bottleneck via Adversarial Information Separation

2026-02-06

AI Summary

This work addresses the challenge of extracting task-relevant features from experimental data in data-scarce domains such as materials science, where such features are often severely corrupted by noise and artifacts, rendering conventional methods ineffective at disentangling them. To this end, the authors propose the Adversarial Information Separation Framework (AdverISF), which enforces disentanglement between task-specific features and noise representations through self-supervised adversarial learning coupled with statistical independence constraints. A multi-layer separation architecture is further introduced to iteratively recycle noise information across feature hierarchies, thereby recovering useful signals mistakenly classified as noise. Notably, the method operates without explicit supervision labels and achieves significantly finer-grained feature extraction, demonstrating superior generalization performance over state-of-the-art approaches in real-world materials design tasks.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Adversarial Search

Application Category

• Web Mining and Content Analysis: Machine learning and data science for the Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

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.

Problem

• information bottleneck
• feature separation
• measurement noise
• data scarcity
• generalization

Innovation

• Adversarial Information Separation
• Self-supervised Learning
• Information Bottleneck
• Feature Disentanglement
• Multi-layer Separation Architecture