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

05/20/2020
by   Zifan Wang, et al.
0

Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions. In this paper, we develop a novel post-hoc visual explanation method called Score-CAM based on class activation mapping. Unlike previous class activation mapping based approaches, Score-CAM gets rid of the dependence on gradients by obtaining the weight of each activation map through its forward passing score on target class, the final result is obtained by a linear combination of weights and activation maps. We demonstrate that Score-CAM achieves better visual performance with less noise and is fairer than Grad-CAM and Grad-CAM++ for interpreting the decision making process. Our approach outperforms previous methods on both recognition and localization tasks, it also passes the sanity check. We also indicate its application as debugging tools. Official code will be released soon.

READ FULL TEXT

page 1

page 5

page 6

page 7

page 8

page 10

page 11

research
10/03/2019

Score-CAM:Improved Visual Explanations Via Score-Weighted Class Activation Mapping

Recently, more and more attention has been drawn into the internal mecha...
research
08/07/2022

Shap-CAM: Visual Explanations for Convolutional Neural Networks based on Shapley Value

Explaining deep convolutional neural networks has been recently drawing ...
research
06/17/2022

FD-CAM: Improving Faithfulness and Discriminability of Visual Explanation for CNNs

Class activation map (CAM) has been widely studied for visual explanatio...
research
02/10/2021

LIFT-CAM: Towards Better Explanations for Class Activation Mapping

Increasing demands for understanding the internal behaviors of convoluti...
research
07/08/2022

Abs-CAM: A Gradient Optimization Interpretable Approach for Explanation of Convolutional Neural Networks

The black-box nature of Deep Neural Networks (DNNs) severely hinders its...
research
03/01/2023

SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective

Researchers have proposed various methods for visually interpreting the ...
research
03/25/2021

Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks

In this paper, we propose an efficient saliency map generation method, c...

Please sign up or login with your details

Forgot password? Click here to reset