Leveraging Local Structure for Improving Model Explanations: An Information Propagation Approach
This work addresses the need for more coherent and human-aligned explanations in image classification, though it is incremental as it builds on existing attribution methods.
The paper tackles the problem of independent pixel attribution in deep neural network explanations by proposing IProp, a method that jointly evaluates pixels with their structurally-similar neighbors using information propagation, resulting in significant improvements across various interpretability metrics.
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.