2021
Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation
Sam Sattarzadeh, Mahesh Sudhakar, Anthony Lem, Shervin Mehryar, Konstantinos N Plataniotis, Jongseong Jang +4
AAAI 2021technical
As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs). To achieve visual explanations for CNNs, methods based on class activation mapping and randomized input sampling have ga…