From attribution maps to human-understandable explanations through Concept Relevance Propagation
The field of explainable artificial intelligence (XAI) aims to bring transparency to today’s powerful but opaque deep learning models. While local XAI methods explain individual predictions in the form of attribution maps, thereby identifying ‘where’ important features occur (but not providing infor...
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Published in | Nature machine intelligence Vol. 5; no. 9; pp. 1006 - 1019 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
01.09.2023
Nature Publishing Group |
Subjects | |
Online Access | Get full text |
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