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Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks · Around

2017

Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

Chattopadhyay, Aditya, Sarkar, Anirban, Howlader, Prantik et al.

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Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.

However, these deep models are perceived as "black box" methods considering the lack of understanding of their internal functioning.
There has been a significant recent interest in developing explainable deep learning models, and this paper is an effort in this direction.
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 occurrences of multiple object instances in a single image, when compared to state-of-the-art.

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