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Saliency guided data augmentation strategy for maximally utilizing an object’s visual information
Among the various types of data augmentation strategies, the mixup-based approach has been particularly studied. However, in existing mixup-based approaches, object loss and label mismatching can occur if random patches are utilized when constructing augmented images, and additionally, patches that...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9560504/ https://www.ncbi.nlm.nih.gov/pubmed/36227912 http://dx.doi.org/10.1371/journal.pone.0274767 |
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author | An, Junhyeok Jang, Soojin Kwon, Junehyoung Jin, Kyohoon Kim, YoungBin |
author_facet | An, Junhyeok Jang, Soojin Kwon, Junehyoung Jin, Kyohoon Kim, YoungBin |
author_sort | An, Junhyeok |
collection | PubMed |
description | Among the various types of data augmentation strategies, the mixup-based approach has been particularly studied. However, in existing mixup-based approaches, object loss and label mismatching can occur if random patches are utilized when constructing augmented images, and additionally, patches that do not contain objects might be included, which degrades performance. In this paper, we propose a novel augmentation method that mixes patches in a non-overlapping manner after they are extracted from the salient regions in an image. The suggested method can make effective use of object characteristics, because the constructed image consists only of visually important regions and is robust to noise. Since the patches do not occlude each other, the semantically meaningful information in the salient regions can be fully utilized. Additionally, our method is more robust to adversarial attack than the conventional augmentation method. In the experimental results, when Wide ResNet was trained on the public datasets, CIFAR-10, CIFAR-100 and STL-10, the top-1 accuracy was 97.26%, 83.99% and 82.40% respectively, which surpasses other augmentation methods. |
format | Online Article Text |
id | pubmed-9560504 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-95605042022-10-14 Saliency guided data augmentation strategy for maximally utilizing an object’s visual information An, Junhyeok Jang, Soojin Kwon, Junehyoung Jin, Kyohoon Kim, YoungBin PLoS One Research Article Among the various types of data augmentation strategies, the mixup-based approach has been particularly studied. However, in existing mixup-based approaches, object loss and label mismatching can occur if random patches are utilized when constructing augmented images, and additionally, patches that do not contain objects might be included, which degrades performance. In this paper, we propose a novel augmentation method that mixes patches in a non-overlapping manner after they are extracted from the salient regions in an image. The suggested method can make effective use of object characteristics, because the constructed image consists only of visually important regions and is robust to noise. Since the patches do not occlude each other, the semantically meaningful information in the salient regions can be fully utilized. Additionally, our method is more robust to adversarial attack than the conventional augmentation method. In the experimental results, when Wide ResNet was trained on the public datasets, CIFAR-10, CIFAR-100 and STL-10, the top-1 accuracy was 97.26%, 83.99% and 82.40% respectively, which surpasses other augmentation methods. Public Library of Science 2022-10-13 /pmc/articles/PMC9560504/ /pubmed/36227912 http://dx.doi.org/10.1371/journal.pone.0274767 Text en © 2022 An 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, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article An, Junhyeok Jang, Soojin Kwon, Junehyoung Jin, Kyohoon Kim, YoungBin Saliency guided data augmentation strategy for maximally utilizing an object’s visual information |
title | Saliency guided data augmentation strategy for maximally utilizing an object’s visual information |
title_full | Saliency guided data augmentation strategy for maximally utilizing an object’s visual information |
title_fullStr | Saliency guided data augmentation strategy for maximally utilizing an object’s visual information |
title_full_unstemmed | Saliency guided data augmentation strategy for maximally utilizing an object’s visual information |
title_short | Saliency guided data augmentation strategy for maximally utilizing an object’s visual information |
title_sort | saliency guided data augmentation strategy for maximally utilizing an object’s visual information |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9560504/ https://www.ncbi.nlm.nih.gov/pubmed/36227912 http://dx.doi.org/10.1371/journal.pone.0274767 |
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