LocMix: local saliency-based data augmentation for image classification
Data augmentation is a crucial strategy to tackle issues like inadequate model robustness and a significant generalization gap. It is proven to combat overfitting, elevate deep neural network performance, and enhance generalization, particularly when data are limited. In recent years, mixed sample d...
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Veröffentlicht in: | Signal, image and video processing image and video processing, 2024-03, Vol.18 (2), p.1383-1392 |
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creator | Yan, Lingyu Ye, Yu Wang, Chunzhi Sun, Yun |
description | Data augmentation is a crucial strategy to tackle issues like inadequate model robustness and a significant generalization gap. It is proven to combat overfitting, elevate deep neural network performance, and enhance generalization, particularly when data are limited. In recent years, mixed sample data augmentation (MSDA), including variants like Mixup and CutMix, has gained significant attention. However, these methods sometimes confound the network with misleading signals, limiting their effectiveness. In this context, we propose LocMix, an MSDA that aims to generate new training samples by prioritizing local saliency feature information and employing statistical data mixing. We achieve this by concealing salient regions with random masks and efficiently combining images through the optimization of local saliency information using transportation methods. Prioritizing the local features within an image allows LocMix to capture image details with greater accuracy and comprehensiveness, thereby enhancing the model’s capacity to understand the target image. We conduct extensive validation of this approach on various challenging datasets. When applied to the training of the PreAct-ResNet18 model, our method yields notable improvements in accuracy. Specifically, on the CIFAR-10 dataset, we observe an impressive 1.71% accuracy enhancement. Similarly, on CIFAR-100, Tiny-ImageNet, ImageNet, and SVHN, we attain substantial accuracy improvements of 80.12%, 64.60%, 77.62%, and 97.12%, corresponding to improvements of 4.88%, 8.75%, 1.93%, and 0.57%, respectively. These experimental results plainly illustrate the effectiveness of our proposed method. |
doi_str_mv | 10.1007/s11760-023-02852-0 |
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We conduct extensive validation of this approach on various challenging datasets. When applied to the training of the PreAct-ResNet18 model, our method yields notable improvements in accuracy. Specifically, on the CIFAR-10 dataset, we observe an impressive 1.71% accuracy enhancement. Similarly, on CIFAR-100, Tiny-ImageNet, ImageNet, and SVHN, we attain substantial accuracy improvements of 80.12%, 64.60%, 77.62%, and 97.12%, corresponding to improvements of 4.88%, 8.75%, 1.93%, and 0.57%, respectively. 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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-985d43c65506b0591464d56a4a9e3e0336fc5483172895df26275a8e6f93ae393</citedby><cites>FETCH-LOGICAL-c319t-985d43c65506b0591464d56a4a9e3e0336fc5483172895df26275a8e6f93ae393</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11760-023-02852-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11760-023-02852-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids></links><search><creatorcontrib>Yan, Lingyu</creatorcontrib><creatorcontrib>Ye, Yu</creatorcontrib><creatorcontrib>Wang, Chunzhi</creatorcontrib><creatorcontrib>Sun, Yun</creatorcontrib><title>LocMix: local saliency-based data augmentation for image classification</title><title>Signal, image and video processing</title><addtitle>SIViP</addtitle><description>Data augmentation is a crucial strategy to tackle issues like inadequate model robustness and a significant generalization gap. 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We conduct extensive validation of this approach on various challenging datasets. When applied to the training of the PreAct-ResNet18 model, our method yields notable improvements in accuracy. Specifically, on the CIFAR-10 dataset, we observe an impressive 1.71% accuracy enhancement. Similarly, on CIFAR-100, Tiny-ImageNet, ImageNet, and SVHN, we attain substantial accuracy improvements of 80.12%, 64.60%, 77.62%, and 97.12%, corresponding to improvements of 4.88%, 8.75%, 1.93%, and 0.57%, respectively. These experimental results plainly illustrate the effectiveness of our proposed method.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s11760-023-02852-0</doi><tpages>10</tpages></addata></record> |
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subjects | Accuracy Artificial neural networks Computer Imaging Computer Science Data augmentation Datasets Effectiveness Image classification Image enhancement Image Processing and Computer Vision Multimedia Information Systems Original Paper Pattern Recognition and Graphics Salience Signal,Image and Speech Processing Vision |
title | LocMix: local saliency-based data augmentation for image classification |
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