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SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map
Modern data augmentation strategies such as Cutout, Mixup, and CutMix, have achieved good performance in image recognition tasks. Particularly, the data augmentation approaches, such as Mixup and CutMix, that mix two images to generate a mixed training image, could generalize convolutional neural ne...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706478/ https://www.ncbi.nlm.nih.gov/pubmed/34960539 http://dx.doi.org/10.3390/s21248444 |
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author | Choi, Jaehyeop Lee, Chaehyeon Lee, Donggyu Jung, Heechul |
author_facet | Choi, Jaehyeop Lee, Chaehyeon Lee, Donggyu Jung, Heechul |
author_sort | Choi, Jaehyeop |
collection | PubMed |
description | Modern data augmentation strategies such as Cutout, Mixup, and CutMix, have achieved good performance in image recognition tasks. Particularly, the data augmentation approaches, such as Mixup and CutMix, that mix two images to generate a mixed training image, could generalize convolutional neural networks better than single image-based data augmentation approaches such as Cutout. We focus on the fact that the mixed image can improve generalization ability, and we wondered if it would be effective to apply it to a single image. Consequently, we propose a new data augmentation method to produce a self-mixed image based on a saliency map, called SalfMix. Furthermore, we combined SalfMix with state-of-the-art two images-based approaches, such as Mixup, SaliencyMix, and CutMix, to increase the performance, called HybridMix. The proposed SalfMix achieved better accuracies than Cutout, and HybridMix achieved state-of-the-art performance on three classification datasets: CIFAR-10, CIFAR-100, and TinyImageNet-200. Furthermore, HybridMix achieved the best accuracy in object detection tasks on the VOC dataset, in terms of mean average precision. |
format | Online Article Text |
id | pubmed-8706478 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87064782021-12-25 SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map Choi, Jaehyeop Lee, Chaehyeon Lee, Donggyu Jung, Heechul Sensors (Basel) Article Modern data augmentation strategies such as Cutout, Mixup, and CutMix, have achieved good performance in image recognition tasks. Particularly, the data augmentation approaches, such as Mixup and CutMix, that mix two images to generate a mixed training image, could generalize convolutional neural networks better than single image-based data augmentation approaches such as Cutout. We focus on the fact that the mixed image can improve generalization ability, and we wondered if it would be effective to apply it to a single image. Consequently, we propose a new data augmentation method to produce a self-mixed image based on a saliency map, called SalfMix. Furthermore, we combined SalfMix with state-of-the-art two images-based approaches, such as Mixup, SaliencyMix, and CutMix, to increase the performance, called HybridMix. The proposed SalfMix achieved better accuracies than Cutout, and HybridMix achieved state-of-the-art performance on three classification datasets: CIFAR-10, CIFAR-100, and TinyImageNet-200. Furthermore, HybridMix achieved the best accuracy in object detection tasks on the VOC dataset, in terms of mean average precision. MDPI 2021-12-17 /pmc/articles/PMC8706478/ /pubmed/34960539 http://dx.doi.org/10.3390/s21248444 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Choi, Jaehyeop Lee, Chaehyeon Lee, Donggyu Jung, Heechul SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map |
title | SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map |
title_full | SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map |
title_fullStr | SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map |
title_full_unstemmed | SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map |
title_short | SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map |
title_sort | salfmix: a novel single image-based data augmentation technique using a saliency map |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706478/ https://www.ncbi.nlm.nih.gov/pubmed/34960539 http://dx.doi.org/10.3390/s21248444 |
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