Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement

TL;DR

This paper introduces Concept Distillation, a method that leverages human-centered concept explanations to improve neural network training by reducing bias, enhancing interpretability, and incorporating prior knowledge through concept-sensitive fine-tuning and distillation.

Contribution

It extends Concept Activation Vectors from post-hoc analysis to ante-hoc training, generalizes concepts to intermediate layers, and introduces Concept Distillation for richer concept learning using a teacher model.

Findings

Concept-sensitive training reduces model bias.

The method improves interpretability of neural networks.

It effectively incorporates prior knowledge into models.

Abstract

Humans use abstract concepts for understanding instead of hard features. Recent interpretability research has focused on human-centered concept explanations of neural networks. Concept Activation Vectors (CAVs) estimate a model's sensitivity and possible biases to a given concept. In this paper, we extend CAVs from post-hoc analysis to ante-hoc training in order to reduce model bias through fine-tuning using an additional Concept Loss. Concepts were defined on the final layer of the network in the past. We generalize it to intermediate layers using class prototypes. This facilitates class learning in the last convolution layer, which is known to be most informative. We also introduce Concept Distillation to create richer concepts using a pre-trained knowledgeable model as the teacher. Our method can sensitize or desensitize a model towards concepts. We show applications of concept-sensitive training to debias several classification problems. We also use concepts to induce prior knowledge into IID, a reconstruction problem. Concept-sensitive training can improve model interpretability, reduce biases, and induce prior knowledge. Please visit https://avani17101.github.io/Concept-Distilllation/ for code and more details.