1 citations · 1 across the 2 of their papers we have counts for
5 papers
ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification
Zhibo Zhang, Jongseong Jang, Chiheb Trabelsi +4
Contrastive learning has led to substantial improvements in the quality of learned embedding representations for tasks such as image classification. However, a key drawback of exis…
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness
Ruiwen Li, Zhibo Zhang, Jiani Li +5
Recent years have seen the introduction of a range of methods for post-hoc explainability of image classifier predictions. However, these post-hoc explanations may not always be fa…
Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring
Sam Sattarzadeh, Mahesh Sudhakar, Konstantinos N. Plataniotis +3
Visualizing the features captured by Convolutional Neural Networks (CNNs) is one of the conventional approaches to interpret the predictions made by these models in numerous image…
Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks
Mahesh Sudhakar, Sam Sattarzadeh, Konstantinos N. Plataniotis +3
Explainable AI (XAI) is an active research area to interpret a neural network's decision by ensuring transparency and trust in the task-specified learned models. Recently, perturba…
Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation
Sam Sattarzadeh, Mahesh Sudhakar, Anthony Lem +7
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).…