Extracting Interpretable Concept-Based Decision Trees from CNNs
arXiv:1906.04664
Abstract
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.
arXiv commentpresented at 2019 ICML Workshop on Human in the Loop Learning (HILL 2019), Long Beach, USA
References in corpus (6)
- Scikit-learn: Machine Learning in Python
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
- Distilling a Neural Network Into a Soft Decision Tree
- Effects of Degradations on Deep Neural Network Architectures
- Interpreting CNNs via Decision Trees
- Neural Decision Trees