WWW: A Unified Framework for Explaining what, Where and why of Neural Networks by Interpretation of Neuron Concepts

Abstract

Recent advancements in neural networks have show-cased their remarkable capabilities across various do-mains. Despite these successes, the “black box” problem still remains. To address this, we propose a novel frame-work, www, that offers the ‘what’, ‘where’, and ‘why’ of the neural network decisions in human-understandable terms. Specifically, WWW utilizes adaptive selection for concept discovery, employing adaptive cosine simi-larity and thresholding techniques to effectively explain ‘what’. To address the ‘where’ and ‘why’, we proposed a novel combination of neuron activation maps (NAMs) with Shapley values, generating localized concept maps and heatmaps for individual inputs. Furthermore, WWW in-troduces a method for predicting uncertainty, leveraging heatmap similarities to estimate the prediction's reliability. Experimental evaluations of WWW demonstrate superior performance in both quantitative and qualitative metrics, outperforming existing methods in interpretability. WWW provides a unified solution for explaining ‘what’, ‘where’, and ‘why’, introducing a methodfor localized explanations from global interpretations and offering a plug-and-play so-lution adaptable to various architectures. Code is available at: https://github.comlailab-kyungheelWWW