Cross-model consensus of explanations and beyond for image classification models: an empirical study

DOI10.1007/S10994-023-06312-1arXiv2109.00707OpenAlexW3198826142MaRDI QIDQ6103575

Dejing Dou, Xuhong Li, Siyu Huang, Haoyi Xiong, Shilei Ji

Publication date: 27 June 2023

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2109.00707

zbMATH Keywords

interpretabilitysemantic segmentationand visualizationexplanations of deep neural networks

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Distilling ensemble of explanations for weakly-supervised pre-training of image segmentation models

Cites Work

• All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
• A decision-theoretic approach for model interpretability in Bayesian framework

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