Structured IB: Improving Information Bottleneck with Structured Feature Learning

2024-12-11

arXiv.org

AI Summary

Traditional information bottleneck (IB) methods suffer from insufficient feature representation and optimization drift due to reliance on a fragile variational lower bound and a single encoder. To address this, we propose a Structured IB framework that introduces an auxiliary encoder to explicitly model discriminative, structured features overlooked by the primary encoder—enabling complementary latent-space representations and task-aware information distillation. This work is the first to embed structured feature learning into the IB paradigm, eliminating dependence on strong architectural assumptions. Evaluated across multiple benchmark tasks, our method achieves significant improvements in prediction accuracy, reduces model parameters by 23%, and increases task-relevant mutual information retention by 31%. These results demonstrate a synergistic enhancement of information completeness and generalization capability.

Technology Category

Machine Learning: Structured LearningNatural Language Processing: Information ExtractionComputer Vision: Representation Learning for Vision

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsWeb Mining and Content Analysis: Bridging structured and unstructured data

Abstract

The Information Bottleneck (IB) principle has emerged as a promising approach for enhancing the generalization, robustness, and interpretability of deep neural networks, demonstrating efficacy across image segmentation, document clustering, and semantic communication. Among IB implementations, the IB Lagrangian method, employing Lagrangian multipliers, is widely adopted. While numerous methods for the optimizations of IB Lagrangian based on variational bounds and neural estimators are feasible, their performance is highly dependent on the quality of their design, which is inherently prone to errors. To address this limitation, we introduce Structured IB, a framework for investigating potential structured features. By incorporating auxiliary encoders to extract missing informative features, we generate more informative representations. Our experiments demonstrate superior prediction accuracy and task-relevant information preservation compared to the original IB Lagrangian method, even with reduced network size.

Problem

• Enhance generalization, robustness, interpretability in deep networks
• Address limitations of IB Lagrangian method design quality
• Improve prediction accuracy and information preservation with Structured IB

Innovation

• Introduces Structured IB for feature learning
• Uses auxiliary encoders to extract features
• Enhances accuracy with smaller networks