Our extensive experiments and evaluations, both subjective and objective, on standard datasets showed that Grad-CAM++ provides promising human-interpretable visual explanations for a given CNN architecture across multiple tasks including c…

We provide a mathematical derivation for the proposed method, which uses a weighted combination of the positive partial derivatives of the last convolutional layer feature maps with respect to a specific class score as weights to generate …

Building on a recently proposed method called Grad-CAM, we propose a generalized method called Grad-CAM++ that can provide better visual explanations of CNN model predictions, in terms of better object localization as well as explaining oc…

There has been a significant recent interest in developing explainable deep learning models, and this paper is an effort in this direction. Aditya Chattopadhyay, et al., "Grad-CAM++: Improved Visual Explanations for Deep Convolutional Netw…

However, these deep models are perceived as ”black box” methods considering the lack of understanding of their internal functioning. Aditya Chattopadhyay, et al., "Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks" h…

Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems. Aditya Chattopadhyay, et al., "Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks" https:…