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Improve the Deep Learning Models in Forestry Based on Explanations and Expertise
In forestry studies, deep learning models have achieved excellent performance in many application scenarios (e.g., detecting forest damage). However, the unclear model decisions (i.e., black-box) undermine the credibility of the results and hinder their practicality. This study intends to obtain exp...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9169801/ https://www.ncbi.nlm.nih.gov/pubmed/35677249 http://dx.doi.org/10.3389/fpls.2022.902105 |
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author | Cheng, Ximeng Doosthosseini, Ali Kunkel, Julian |
author_facet | Cheng, Ximeng Doosthosseini, Ali Kunkel, Julian |
author_sort | Cheng, Ximeng |
collection | PubMed |
description | In forestry studies, deep learning models have achieved excellent performance in many application scenarios (e.g., detecting forest damage). However, the unclear model decisions (i.e., black-box) undermine the credibility of the results and hinder their practicality. This study intends to obtain explanations of such models through the use of explainable artificial intelligence methods, and then use feature unlearning methods to improve their performance, which is the first such attempt in the field of forestry. Results of three experiments show that the model training can be guided by expertise to gain specific knowledge, which is reflected by explanations. For all three experiments based on synthetic and real leaf images, the improvement of models is quantified in the classification accuracy (up to 4.6%) and three indicators of explanation assessment (i.e., root-mean-square error, cosine similarity, and the proportion of important pixels). Besides, the introduced expertise in annotation matrix form was automatically created in all experiments. This study emphasizes that studies of deep learning in forestry should not only pursue model performance (e.g., higher classification accuracy) but also focus on the explanations and try to improve models according to the expertise. |
format | Online Article Text |
id | pubmed-9169801 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91698012022-06-07 Improve the Deep Learning Models in Forestry Based on Explanations and Expertise Cheng, Ximeng Doosthosseini, Ali Kunkel, Julian Front Plant Sci Plant Science In forestry studies, deep learning models have achieved excellent performance in many application scenarios (e.g., detecting forest damage). However, the unclear model decisions (i.e., black-box) undermine the credibility of the results and hinder their practicality. This study intends to obtain explanations of such models through the use of explainable artificial intelligence methods, and then use feature unlearning methods to improve their performance, which is the first such attempt in the field of forestry. Results of three experiments show that the model training can be guided by expertise to gain specific knowledge, which is reflected by explanations. For all three experiments based on synthetic and real leaf images, the improvement of models is quantified in the classification accuracy (up to 4.6%) and three indicators of explanation assessment (i.e., root-mean-square error, cosine similarity, and the proportion of important pixels). Besides, the introduced expertise in annotation matrix form was automatically created in all experiments. This study emphasizes that studies of deep learning in forestry should not only pursue model performance (e.g., higher classification accuracy) but also focus on the explanations and try to improve models according to the expertise. Frontiers Media S.A. 2022-05-19 /pmc/articles/PMC9169801/ /pubmed/35677249 http://dx.doi.org/10.3389/fpls.2022.902105 Text en Copyright © 2022 Cheng, Doosthosseini and Kunkel. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Cheng, Ximeng Doosthosseini, Ali Kunkel, Julian Improve the Deep Learning Models in Forestry Based on Explanations and Expertise |
title | Improve the Deep Learning Models in Forestry Based on Explanations and Expertise |
title_full | Improve the Deep Learning Models in Forestry Based on Explanations and Expertise |
title_fullStr | Improve the Deep Learning Models in Forestry Based on Explanations and Expertise |
title_full_unstemmed | Improve the Deep Learning Models in Forestry Based on Explanations and Expertise |
title_short | Improve the Deep Learning Models in Forestry Based on Explanations and Expertise |
title_sort | improve the deep learning models in forestry based on explanations and expertise |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9169801/ https://www.ncbi.nlm.nih.gov/pubmed/35677249 http://dx.doi.org/10.3389/fpls.2022.902105 |
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