Semantic interpretation for convolutional neural networks: What makes a cat a cat?

TL;DR

This paper introduces S-XAI, a framework that enhances the interpretability of CNNs by extracting semantic spaces using PCA, genetic algorithms, and visualization, enabling better understanding of model decisions.

Contribution

The paper presents a novel semantic explainable AI framework that combines PCA, genetic algorithms, and visualization to interpret CNNs semantically, introducing the concept of semantic probability.

Findings

S-XAI effectively interprets CNNs semantically.

Enables trustworthiness assessment of neural networks.

Facilitates semantic sample searching.

Abstract

The interpretability of deep neural networks has attracted increasing attention in recent years, and several methods have been created to interpret the "black box" model. Fundamental limitations remain, however, that impede the pace of understanding the networks, especially the extraction of understandable semantic space. In this work, we introduce the framework of semantic explainable AI (S-XAI), which utilizes row-centered principal component analysis to obtain the common traits from the best combination of superpixels discovered by a genetic algorithm, and extracts understandable semantic spaces on the basis of discovered semantically sensitive neurons and visualization techniques. Statistical interpretation of the semantic space is also provided, and the concept of semantic probability is proposed for the first time. Our experimental results demonstrate that S-XAI is effective in providing a semantic interpretation for the CNN, and offers broad usage, including trustworthiness assessment and semantic sample searching.