Overview / Usage

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.