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In this work, we propose CLass-Enhanced Attentive Response (CLEAR): anapproach to visualize and understand the decisions made by deep neural networks(DNNs) given a specific input. CLEAR facilitates the visualization of attentiveregions and levels of interest of DNNs during the decision-making process. Italso enables the visualization of the most dominant classes associated withthese attentive regions of interest. As such, CLEAR can mitigate some of theshortcomings of heatmap-based methods associated with decision ambiguity, andallows for better insights into the decision-making process of DNNs.Quantitative and qualitative experiments across three different datasetsdemonstrate the efficacy of CLEAR for gaining a better understanding of theinner workings of DNNs during the decision-making process.

Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks