# Extracting Interpretable Concept-Based Decision Trees from CNNs

arXiv: https://arxiv.org/abs/1906.04664 · HTML: https://mlsanity.com/paper/1906.04664/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [Concept Embedding Analysis: A Review](https://mlsanity.com/paper/2203.13909/) (2022)
- [Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability](https://mlsanity.com/paper/2304.14864/) (2023)
- [Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces](https://mlsanity.com/paper/2305.07663/) (2023)
- [Interpreting Neural Policies with Disentangled Tree Representations](https://mlsanity.com/paper/2210.06650/) (2022)
- [The Anatomy of Adversarial Attacks: Concept-based XAI Dissection](https://mlsanity.com/paper/2403.16782/) (2024)

## Abstract (arXiv)

In an attempt to gather a deeper understanding of how convolutional neural networks (CNNs) reason about human-understandable concepts, we present a method to infer labeled concept data from hidden layer activations and interpret the concepts through a shallow decision tree. The decision tree can provide information about which concepts a model deems important, as well as provide an understanding of how the concepts interact with each other. Experiments demonstrate that the extracted decision tree is capable of accurately representing the original CNN's classifications at low tree depths, thus encouraging human-in-the-loop understanding of discriminative concepts.