SLRP: Improved heatmap generation via selective layer-wise relevance propagation

Abstract

Deep learning has been recently applied to various areas of artificial intelligence, where it has displayed excellent performance. However, many deep-learning models are a black box, which makes it difficult to interpret the models and understand the predictions. Explainability is crucial for critical real-world systems (in the fields such as defense, aerospace, and security). To solve this problem, the concept of explainable artificial intelligence has emerged. For image classification, various approaches have been proposed to visually explain the model's prediction. A typical approach is layer-wise relevance propagation, which generates a heatmap, where each pixel value represents the contributions to the model's predictions. However, even advanced versions of layer-wise relevance propagation (such as contrastive layer-wise relevance propagation and softmax-gradient layer-wise relevance propagation) have some limitations. Here, selective layer-wise relevance propagation, which generates a clearer heatmap than the existing methods by combining relevance-based methods and gradient-based methods is proposed. To evaluate the proposed method and verify its effectiveness, we conduct comparative experiments. Qualitative and quantitative results show that selective layer-wise relevance propagation produces less noisy, class-discriminative, and object-preserving results. The proposed method can be used to improve the explainability of deep-learning models in image classification.