Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study

no code implementations • 2 Sep 2021 • Xuhong LI, Haoyi Xiong, Siyu Huang, Shilei Ji, Dejing Dou

Existing interpretation algorithms have found that, even deep models make the same and right predictions on the same image, they might rely on different sets of input features for classification.

Attribute Image Classification +2

From Distributed Machine Learning to Federated Learning: A Survey

no code implementations • 29 Apr 2021 • Ji Liu, Jizhou Huang, Yang Zhou, Xuhong LI, Shilei Ji, Haoyi Xiong, Dejing Dou

Because of laws or regulations, the distributed data and computing resources cannot be directly shared among different regions or organizations for machine learning tasks.

BIG-bench Machine Learning Federated Learning

Democratizing Evaluation of Deep Model Interpretability through Consensus

no code implementations • 1 Jan 2021 • Xuhong LI, Haoyi Xiong, Siyu Huang, Shilei Ji, Yanjie Fu, Dejing Dou

Given any task/dataset, Consensus first obtains the interpretation results using existing tools, e. g., LIME (Ribeiro et al., 2016), for every model in the committee, then aggregates the results from the entire committee and approximates the “ground truth” of interpretations through voting.

Feature Importance