Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat? (Adv. Sci. 35/2022)

Interpretable Machine Learning The semantic explainable AI (S‐XAI) discovers what makes a cat to be recognized as a cat in the convolutional neural networks by extracting common traits and establishing semantic space from diversified samples of cats. The visualized common traits contain identifiable...

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Detalles Bibliográficos
Autores principales: Xu, Hao, Chen, Yuntian, Zhang, Dongxiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Cover Picture
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762285/
http://dx.doi.org/10.1002/advs.202270221
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author Xu, Hao
Chen, Yuntian
Zhang, Dongxiao
author_facet Xu, Hao
Chen, Yuntian
Zhang, Dongxiao
author_sort Xu, Hao
collection PubMed
description Interpretable Machine Learning The semantic explainable AI (S‐XAI) discovers what makes a cat to be recognized as a cat in the convolutional neural networks by extracting common traits and establishing semantic space from diversified samples of cats. The visualized common traits contain identifiable semantic concepts like eyes, noses and beards, which give a semantic interpretation for the convolutional neural networks. The S‐XAI has a promising prospect in the aspect of trustworthiness assessment and semantic sample searching. More details can be found in the article number 2204723 by Hao Xu, Yuntian Chen, and Dongxiao Zhang. [Image: see text]
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spelling pubmed-97622852022-12-20 Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat? (Adv. Sci. 35/2022) Xu, Hao Chen, Yuntian Zhang, Dongxiao Adv Sci (Weinh) Cover Picture Interpretable Machine Learning The semantic explainable AI (S‐XAI) discovers what makes a cat to be recognized as a cat in the convolutional neural networks by extracting common traits and establishing semantic space from diversified samples of cats. The visualized common traits contain identifiable semantic concepts like eyes, noses and beards, which give a semantic interpretation for the convolutional neural networks. The S‐XAI has a promising prospect in the aspect of trustworthiness assessment and semantic sample searching. More details can be found in the article number 2204723 by Hao Xu, Yuntian Chen, and Dongxiao Zhang. [Image: see text] John Wiley and Sons Inc. 2022-12-19 /pmc/articles/PMC9762285/ http://dx.doi.org/10.1002/advs.202270221 Text en © 2022 Wiley‐VCH GmbH https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Cover Picture
Xu, Hao
Chen, Yuntian
Zhang, Dongxiao
Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat? (Adv. Sci. 35/2022)
title Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat? (Adv. Sci. 35/2022)
title_full Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat? (Adv. Sci. 35/2022)
title_fullStr Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat? (Adv. Sci. 35/2022)
title_full_unstemmed Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat? (Adv. Sci. 35/2022)
title_short Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat? (Adv. Sci. 35/2022)
title_sort semantic interpretation for convolutional neural networks: what makes a cat a cat? (adv. sci. 35/2022)
topic Cover Picture
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762285/
http://dx.doi.org/10.1002/advs.202270221
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