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LMix: regularization strategy for convolutional neural networks

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Abstract

Deep convolutional neural networks perform well in the field of computer vision, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. Therefore, proper regularization strategies are needed to alleviate these problems. Currently, regularization strategies with mixed sample data augmentation perform very well, and these algorithms allow the network to generalize better, improve the baseline performance of the model. However, interpolation-based mixed sample data augmentation distorts the data distribution, while masking-based mixed sample data augmentation results in excessive information loss for overly regular shapes of masks. Although mixed sample data augmentation is proven to be an effective method to improve the baseline performance, generalization ability and robustness of deep convolutional models, there is still room for improvement in terms of maintaining the of image local consistency and image data distribution. In this paper, we propose a new mixed sample data augmentation-LMix, which uses random masking to increase the number of masks in the image to maintain the data distribution, and high-frequency filtering to sharpen the image to highlight recognition regions. We applied the method to train CIFAR-10, CIFAR-100, SVHN, and Tiny-ImageNet datasets under the PreAct-ResNet18 model to evaluate the method, and obtained the latest results of 96.32, 79.85, 97.01, and 64.16%, respectively, which are 1.70, 4.73, and 8.06% higher than the optimal baseline accuracy. The LMix algorithm improves the generalization ability of the state-of-the-art neural network architecture and enhances the robustness to adversarial samples.

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All of our data sets come from public data sets.You can go to the corresponding official website to download.

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Funding

This work is funded by the National Natural Science Foundation of China under Grant No. 61772180, the Key R D plan of Hubei Province (2020BHB004, 2020BAB012).

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Authors and Affiliations

  1. Hubei University of Technology, Wuhan, Hubei, China

    Linyu Yan, Kunpeng Zheng, Jinyao Xia & Ke Li

  2. Huazhong University of Science and Technology, Wuhan, Hubei, China

    Hefei Ling

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  1. Linyu Yan
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  2. Kunpeng Zheng
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  3. Jinyao Xia
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  4. Ke Li
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  5. Hefei Ling
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Contributions

LY and KZ completed the main manuscript text and experiments. JX prepared Table 4. KL prepared Table 5. All authors reviewed the manuscript.

Corresponding author

Correspondence to Kunpeng Zheng.

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Yan, L., Zheng, K., Xia, J. et al. LMix: regularization strategy for convolutional neural networks. SIViP 17, 1245–1253 (2023). https://doi.org/10.1007/s11760-022-02332-x

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  • DOI: https://doi.org/10.1007/s11760-022-02332-x

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