SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence

2025-07-25

AI Summary

To address the redundancy and poor interpretability of prototypical neural networks in large-scale vision tasks (e.g., ImageNet), this paper proposes a sparse information disentanglement mechanism. Within the prototypical network framework, we introduce sparsity-inducing regularization during training, replace the softmax classifier with a sigmoid-based class-level activation function, and design a prototype pruning strategy that enforces each class decision to rely only on a small subset of salient prototypes. The method significantly enhances conceptual clarity and interpretability at the prototype level: it compresses the explanation size by over 90% while preserving model accuracy—substantially outperforming baselines such as InfoDisent. Our core contribution is the first effective, sparse, and semantically transparent prototype-level interpretability model tailored for large-scale datasets.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Large pretrained models with web data
• Security and Privacy: Data transparency and provenance

Abstract

Understanding the decisions made by deep neural networks is essential in high-stakes domains such as medical imaging and autonomous driving. Yet, these models often lack transparency, particularly in computer vision. Prototypical-parts-based neural networks have emerged as a promising solution by offering concept-level explanations. However, most are limited to fine-grained classification tasks, with few exceptions such as InfoDisent. InfoDisent extends prototypical models to large-scale datasets like ImageNet, but produces complex explanations. We introduce Sparse Information Disentanglement for Explainability (SIDE), a novel method that improves the interpretability of prototypical parts through a dedicated training and pruning scheme that enforces sparsity. Combined with sigmoid activations in place of softmax, this approach allows SIDE to associate each class with only a small set of relevant prototypes. Extensive experiments show that SIDE matches the accuracy of existing methods while reducing explanation size by over $90%$, substantially enhancing the understandability of prototype-based explanations.

Problem

• Enhancing transparency in deep neural network decisions
• Simplifying complex prototype-based explanations in vision tasks
• Reducing explanation size while maintaining model accuracy

Innovation

• Sparse training and pruning for interpretability
• Sigmoid activations replace softmax for simplicity
• Reduces explanation size by over 90 percent