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LocMix: local saliency-based data augmentation for image classification

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Abstract

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.

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Data Availability

Our data sets are sourced from publicly available repositories. To access them, please visit the respective official websites for downloads.

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Funding

This work is funded by the National Natural Science Foundation of China under Grant No. 61772180 and the Key R & D plan of Hubei Province No. 2023BCB041.

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Author notes

  1. Lingyu Yan, Yu Ye have contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science, Hubei University of Technology, Wuhan, 430068, China

    Lingyu Yan, Yu Ye, Chunzhi Wang & Yun Sun

  2. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China

    Yun Sun

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  1. Lingyu Yan
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  2. Yu Ye
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  3. Chunzhi Wang
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  4. Yun Sun
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Contributions

LY and YY performed the main manuscript work and experiments. WC and SY created Tables 3 and 4. All authors participated in manuscript review.

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Correspondence to Yu Ye.

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Yan, L., Ye, Y., Wang, C. et al. LocMix: local saliency-based data augmentation for image classification. SIViP 18, 1383–1392 (2024). https://doi.org/10.1007/s11760-023-02852-0

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