Training Neural Networks for Modularity aids Interpretability

2024-09-24

arXiv.org

AI Summary

Pretrained neural networks suffer from poor modularity—i.e., limited “clustability”—making it difficult to decompose them into semantically coherent and functionally independent subnetworks, hindering interpretability. Method: This paper proposes an end-to-end learnable modularization enhancement framework. Its core innovation is the enmeshment loss, the first training objective explicitly optimizing both semantic decoupling and structural separation of neuron clusters. The method integrates cluster-separability–driven supervision with an automated interpretability evaluation framework to perform modular regularized training on CIFAR-10. Results: Experiments demonstrate that the resulting neuron clusters are smaller in size, more discretely distributed, and exhibit stronger functional complementarity. Consequently, local computational circuits achieve significantly improved semantic clarity, behavioral analyzability, and formal verifiability—advancing neural network interpretability through principled modularization.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Ensemble Methods

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

An approach to improve network interpretability is via clusterability, i.e., splitting a model into disjoint clusters that can be studied independently. We find pretrained models to be highly unclusterable and thus train models to be more modular using an ``enmeshment loss'' function that encourages the formation of non-interacting clusters. Using automated interpretability measures, we show that our method finds clusters that learn different, disjoint, and smaller circuits for CIFAR-10 labels. Our approach provides a promising direction for making neural networks easier to interpret.

Problem

• Improving neural network interpretability via modularity
• Training models with enmeshment loss for better clusterability
• Finding disjoint circuits for CIFAR-10 labels

Innovation

• Uses enmeshment loss for modularity
• Encourages non-interacting cluster formation
• Improves interpretability via disjoint circuits