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Class incremental learning of remote sensing images based on class similarity distillation
When a well-trained model learns a new class, the data distribution differences between the new and old classes inevitably cause catastrophic forgetting in order to perform better in the new class. This behavior differs from human learning. In this article, we propose a class incremental object dete...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557500/ https://www.ncbi.nlm.nih.gov/pubmed/37810339 http://dx.doi.org/10.7717/peerj-cs.1583 |
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author | Shen, Mingge Chen, Dehu Hu, Silan Xu, Gang |
author_facet | Shen, Mingge Chen, Dehu Hu, Silan Xu, Gang |
author_sort | Shen, Mingge |
collection | PubMed |
description | When a well-trained model learns a new class, the data distribution differences between the new and old classes inevitably cause catastrophic forgetting in order to perform better in the new class. This behavior differs from human learning. In this article, we propose a class incremental object detection method for remote sensing images to address the problem of catastrophic forgetting caused by distribution differences among different classes. First, we introduce a class similarity distillation (CSD) loss based on the similarity between new and old class prototypes, ensuring the model’s plasticity to learn new classes and stability to detect old classes. Second, to better extract class similarity features, we propose a global similarity distillation (GSD) loss that maximizes the mutual information between the new class feature and old class features. Additionally, we present a region proposal network (RPN)-based method that assigns positive and negative labels to prevent mislearning issues. Experiments demonstrate that our method is more accurate for class incremental learning on public DOTA and DIOR datasets and significantly improves training efficiency compared to state-of-the-art class incremental object detection methods. |
format | Online Article Text |
id | pubmed-10557500 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105575002023-10-07 Class incremental learning of remote sensing images based on class similarity distillation Shen, Mingge Chen, Dehu Hu, Silan Xu, Gang PeerJ Comput Sci Artificial Intelligence When a well-trained model learns a new class, the data distribution differences between the new and old classes inevitably cause catastrophic forgetting in order to perform better in the new class. This behavior differs from human learning. In this article, we propose a class incremental object detection method for remote sensing images to address the problem of catastrophic forgetting caused by distribution differences among different classes. First, we introduce a class similarity distillation (CSD) loss based on the similarity between new and old class prototypes, ensuring the model’s plasticity to learn new classes and stability to detect old classes. Second, to better extract class similarity features, we propose a global similarity distillation (GSD) loss that maximizes the mutual information between the new class feature and old class features. Additionally, we present a region proposal network (RPN)-based method that assigns positive and negative labels to prevent mislearning issues. Experiments demonstrate that our method is more accurate for class incremental learning on public DOTA and DIOR datasets and significantly improves training efficiency compared to state-of-the-art class incremental object detection methods. PeerJ Inc. 2023-09-27 /pmc/articles/PMC10557500/ /pubmed/37810339 http://dx.doi.org/10.7717/peerj-cs.1583 Text en ©2023 Shen et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Shen, Mingge Chen, Dehu Hu, Silan Xu, Gang Class incremental learning of remote sensing images based on class similarity distillation |
title | Class incremental learning of remote sensing images based on class similarity distillation |
title_full | Class incremental learning of remote sensing images based on class similarity distillation |
title_fullStr | Class incremental learning of remote sensing images based on class similarity distillation |
title_full_unstemmed | Class incremental learning of remote sensing images based on class similarity distillation |
title_short | Class incremental learning of remote sensing images based on class similarity distillation |
title_sort | class incremental learning of remote sensing images based on class similarity distillation |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557500/ https://www.ncbi.nlm.nih.gov/pubmed/37810339 http://dx.doi.org/10.7717/peerj-cs.1583 |
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