# Interpretable Disentanglement of Neural Networks by Extracting Class-Specific Subnetwork

arXiv: https://arxiv.org/abs/1910.02673 · HTML: https://mlsanity.com/paper/1910.02673/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Certified Circuits: Stability Guarantees for Mechanistic Circuits](https://mlsanity.com/paper/2602.22968/) (2026)

## Abstract (arXiv)

We propose a novel perspective to understand deep neural networks in an interpretable disentanglement form. For each semantic class, we extract a class-specific functional subnetwork from the original full model, with compressed structure while maintaining comparable prediction performance. The structure representations of extracted subnetworks display a resemblance to their corresponding class semantic similarities. We also apply extracted subnetworks in visual explanation and adversarial example detection tasks by merely replacing the original full model with class-specific subnetworks. Experiments demonstrate that this intuitive operation can effectively improve explanation saliency accuracy for gradient-based explanation methods, and increase the detection rate for confidence score-based adversarial example detection methods.