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

06/25/2020
by   Rakshit Naidu, et al.
12

Deep Convolution Neural Networks are often referred to as black-box models due to minimal understandings of their internal actions. As an effort to develop more complex explainable deep learning models, many methods have been proposed to reveal the internal mechanism of the decisionmaking process. In this paper, built on the top of Score-CAM, we introduce an enhanced visual explanation in terms of visual sharpness called SS-CAM, which produces sharper localizations of object features within an image by smoothing. We evaluate our method on three well-known datasets: ILSVRC 2012, Stanford40 and PASCAL VOC 2007 dataset. Our approach outperforms when evaluated on both fairness and localization tasks.

READ FULL TEXT

page 3

page 5

research
10/30/2017

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

Over the last decade, Convolutional Neural Network (CNN) models have bee...
research
08/03/2019

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...
research
04/14/2022

Explainable Analysis of Deep Learning Methods for SAR Image Classification

Deep learning methods exhibit outstanding performance in synthetic apert...
research
06/16/2021

Developing a Fidelity Evaluation Approach for Interpretable Machine Learning

Although modern machine learning and deep learning methods allow for com...
research
11/20/2018

A Gray Box Interpretable Visual Debugging Approach for Deep Sequence Learning Model

Deep Learning algorithms are often used as black box type learning and t...
research
06/19/2018

RISE: Randomized Input Sampling for Explanation of Black-box Models

Deep neural networks are increasingly being used to automate data analys...
research
07/24/2019

Visual Interaction with Deep Learning Models through Collaborative Semantic Inference

Automation of tasks can have critical consequences when humans lose agen...

Please sign up or login with your details

Forgot password? Click here to reset