Grad cam visual explanations from deep networks via gradient-based localization
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Grad-CAM is a technique to produce visual explanations for predictions from convolutional neural networks by generating localization heatmaps highlighting the important regions in the image for a specific prediction. It works by taking the gradients of any target concept (like the class score for a particular image classification task) flowing into the final convolutional layer and projecting back onto the feature maps to produce a coarse localization map highlighting the important regions in the image for predicting the concept. The technique does not require any modifications to the network architecture or training procedures. Results show Grad-CAM can provide visual explanations for CNN predictions by highlighting important regions in the input image.
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use, lower error rates, and accelerated processes, leading to optimized global healthcare outcomes.
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