A Protocol for Evaluating Model Interpretation Methods from Visual Explanations

With the continuous development of Convolutional Neural Networks (CNNs), there is an increasing requirement towards the understanding of the representations they internally encode. The task of studying such encoded representations is referred to as model interpretation. Efforts along this direction, despite being proved efficient, stand with two weaknesses. First, there is low semanticity on the feedback they provide which leads toward subjective visualizations. Second, there is no unified protocol for the quantitative evaluation of interpretation methods which makes the comparison between current and future methods complex. To address these issues, we propose a unified evaluation protocol for the quantitative evaluation of interpretation methods. This is achieved by enhancing existing interpretation methods to be capable of generating visual explanations and then linking these explanations with a semantic label. To achieve this, we introduce the Weighted Average Intersection-over-Union (WAIoU) metric to estimate the coverage rate between explanation heatmaps and semantic annotations. This is complemented with an analysis of several binarization techniques for heatmaps, necessary when measuring coverage. Experiments considering several interpretation methods covering different CNN architectures pre-trained on multiple datasets show the effectiveness of the proposed protocol.