Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks
This work addresses the need for better interpretability in deep learning for researchers and practitioners, but it appears incremental as it builds on existing visualization methods.
The authors tackled the problem of understanding deep neural network decisions by proposing CLEAR, an approach that visualizes attentive regions and dominant classes to mitigate ambiguity in heatmap-based methods, with experiments across three datasets demonstrating its efficacy.
In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.