Leveraging Local Structure for Improving Model Explanations: An Information Propagation Approach

TL;DR

This paper introduces IProp, a novel method that enhances model explanations by modeling pixel attribution scores as a dynamic information propagation process, considering local pixel structures for more accurate interpretability.

Contribution

IProp is a new approach that models attribution scores as a propagation process using Markov Reward Processes, improving existing explanation methods by incorporating local pixel structure.

Findings

IProp significantly improves interpretability metrics across various explanation methods.

The method is compatible with multiple attribution techniques and DNN models.

Experimental results demonstrate enhanced explanation quality.

Abstract

Numerous explanation methods have been recently developed to interpret the decisions made by deep neural network (DNN) models. For image classifiers, these methods typically provide an attribution score to each pixel in the image to quantify its contribution to the prediction. However, most of these explanation methods appropriate attribution scores to pixels independently, even though both humans and DNNs make decisions by analyzing a set of closely related pixels simultaneously. Hence, the attribution score of a pixel should be evaluated jointly by considering itself and its structurally-similar pixels. We propose a method called IProp, which models each pixel's individual attribution score as a source of explanatory information and explains the image prediction through the dynamic propagation of information across all pixels. To formulate the information propagation, IProp adopts the Markov Reward Process, which guarantees convergence, and the final status indicates the desired pixels' attribution scores. Furthermore, IProp is compatible with any existing attribution-based explanation method. Extensive experiments on various explanation methods and DNN models verify that IProp significantly improves them on a variety of interpretability metrics.