14 citations · 37 across the 11 of their papers we have counts for
5 papers · 1 filter
Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network
Tong Li, Jiale Deng, Yanyan Shen +3
Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs. Despite the superior performance, insights about the predi…
Two-Stage Holistic and Contrastive Explanation of Image Classification
Weiyan Xie, Xiao-Hui Li, Zhi Lin +3
The need to explain the output of a deep neural network classifier is now widely recognized. While previous methods typically explain a single class in the output, we advocate expl…
Model Debiasing via Gradient-based Explanation on Representation
Jindi Zhang, Luning Wang, Dan Su +3
Machine learning systems produce biased results towards certain demographic groups, known as the fairness problem. Recent approaches to tackle this problem learn a latent code (i.e…
Consistency Regularization for Domain Generalization with Logit Attribution Matching
Han Gao, Kaican Li, Weiyan Xie +5
Domain generalization (DG) is about training models that generalize well under domain shift. Previous research on DG has been conducted mostly in single-source or multi-source sett…
Explanation Strategies for Image Classification in Humans vs. Current Explainable AI
Ruoxi Qi, Yueyuan Zheng, Yi Yang +2
Explainable AI (XAI) methods provide explanations of AI models, but our understanding of how they compare with human explanations remains limited. In image classification, we found…