Grad-CAM++: Generalized Gradient-based Visual Explanations for Deep Convolutional Networks

by   Aditya Chattopadhyay, et al.

Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision based problems. However, deep models are perceived as "black box" methods considering the lack of understanding of their internal functioning. There has been a significant recent interest to develop explainable deep learning models, and this paper is an effort in this direction. Building on a recently proposed method called Grad-CAM, we propose Grad-CAM++ to provide better visual explanations of CNN model predictions (when compared to Grad-CAM), in terms of better localization of objects as well as explaining occurrences of multiple objects of a class in a single image. We provide a mathematical explanation for the proposed method, Grad-CAM++, 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 a visual explanation for the class label under consideration. Our extensive experiments and evaluations, both subjective and objective, on standard datasets showed that Grad-CAM++ indeed provides better visual explanations for a given CNN architecture when compared to Grad-CAM.



There are no comments yet.


page 3

page 4

page 6

page 8

page 9

page 13

page 14


Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models

Gaining insight into how deep convolutional neural network models perfor...

SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization

Deep Convolution Neural Networks are often referred to as black-box mode...

Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

Recently, increasing attention has been drawn to the internal mechani...

Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer's Disease

In this work, we propose three explainable deep learning architectures t...

Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations

The convolutional neural network (CNN) has become a powerful tool for va...

Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers

Recently, Convolutional Neural Network (CNN) has achieved excellent perf...

Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring

Visualizing the features captured by Convolutional Neural Networks (CNNs...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.