Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

ICCV 2017 Ramprasaath R. SelvarajuMichael CogswellAbhishek DasRamakrishna VedantamDevi ParikhDhruv Batra

We propose a technique for producing "visual explanations" for decisions from a large class of CNN-based models, making them more transparent. Our approach - Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept, flowing into the final convolutional layer to produce a coarse localization map highlighting important regions in the image for predicting the concept... (read more)

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