Attribute-guided Relevance Propagation for interpreting image classifier based on Deep Neural Networks

Deep learning techniques have emerged as powerful tools for addressing complex and varied problems, achieving remarkable success across numerous AI domains. Despite their effectiveness, the inherent complexity of deep learning models makes them considered black boxes, reducing their interpretability and reliability. To address this challenge, we propose a novel approach called Attribute-guided Relevance Propagation (ARP). ARP enhances the interpretability of deep learning models by learning attributes from specific layers within a pre-trained image classifier and integrating these attributes into saliency maps. This integration not only improves the saliency maps but also identifies and provides example images related to key regions reflected in the maps. We validate the efficacy of ARP through both quantitative and qualitative evaluations, employing widely recognized image classifiers such as ResNet-50 and ViT trained on the benchmark datasets. • Attribute-guided Relevance Propagation (ARP) improves DNN interpretability. • ARP boosts saliency performance for CNNs and Transformers via attribute learning. • ARP provides real examples to support the interpretability of saliency maps. • ARP is validated through extensive experiments on various models and datasets.