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A CLass-Enhanced Attentive Response (CLEAR) Approa

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A CLass-Enhanced Attentive Response (CLEAR) Approach to Understand Deep Neural Networks

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. The CLEAR framework was eventually tested on the Intel® Movidius™ Neural Compute Stick.

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    Overview:

    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. The CLEAR framework was eventually tested on the Intel® Movidius™ Neural Compute Stick.

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    作者:Intel® Developer Zone
    原文地址:A CLass-Enhanced Attentive Response (CLEAR) Approa, 感谢原作者分享。

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