Skip to main content

Advertisement

Springer Nature Link
Log in

Random walk-based erasing data augmentation for deep learning

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Random erasing uses rectangular areas to erase images, which improves model generalization performance, but there is too much loss of original image information. In this paper, we introduce a novel approach, the improved data augmentation method based on random walk algorithm, called random walk-based erasing (RWE), for training the CNN model. Compared with random erasing, RWE has more information about the original image because it uses scatter points to erase the original image instead of rectangular boxes. We execute experiments related to random erasure and deploy them on existing models, aiming to explore factors that benefit model accuracy. Firstly, we execute a test to determine parameter setting and validate compatibility. Then, the results of our methods on image classification and object detection tasks were obtained. Finally, we compare the impacts that random erasing and RWE make on image classification and object detection tasks, respectively. The results of different tasks indicate that our methods increase the accuracy by \(\sim \)2.00% on CIFAR-100 and map by \(\sim \)0.5% on the VOC-2007 dataset.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13001–13008 (2020)

  2. Fonseca, J., Bacao, F.: Research trends and applications of data augmentation algorithms. arXiv preprint arXiv:2207.08817 (2022)

  3. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  4. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  5. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  6. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)

  7. Van Dyk, D.A., Meng, X.-L.: The art of data augmentation. J. Comput. Graphical Stat. 10(1), 1–50 (2001)

    Article  MathSciNet  Google Scholar 

  8. Deng, X., Zhao, H., Zhang, H., Zhang, J., Mei, B.: Grid self-occlusion: a grid self-occlusion data augmentation for better classification. Signal Image Video Process., pp. 1–9 (2022)

  9. Ribeiro, B., Towsley, D.: Estimating and sampling graphs with multidimensional random walks. In: Proceedings of the 10th ACM SIGCOMM conference on Internet measurement, pp. 390–403 (2010)

  10. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

  11. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)

  12. Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C.: Efficient object localization using convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 648–656 (2015)

  13. Park, S., Park, J., Shin, S.-J., Moon, I.-C.: Adversarial dropout for supervised and semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, AAAI’18/IAAI’18/EAAI’18. AAAI Press (2018)

  14. Yang, T., Zhu, S., Chen, C.: Gradaug: a new regularization method for deep neural networks. Adv. Neural Inf. Process. Syst. 33, 14207–14218 (2020)

    Google Scholar 

  15. Basak, H., Bhattacharya, R., Hussain, R., Chatterjee, A.: An embarrassingly simple consistency regularization method for semi-supervised medical image segmentation. arXiv preprint arXiv:2202.00677 (2022)

  16. Wei, H., Feng, L., Chen, X., An, B.: Combating noisy labels by agreement: a joint training method with co-regularization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13726–13735 (2020)

  17. Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)

  18. Dadboud, F., Patel, V., Mehta, V., Bolic, M., Mantegh, I.: Single-stage UAV detection and classification with YOLOV5: Mosaic data augmentation and PANet. In: 2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–8. IEEE (2021)

  19. Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)

  20. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    Article  Google Scholar 

  21. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  22. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision, pp. 630–645. Springer, Berlin (2016)

  23. Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)

Download references

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

  1. Automation College, Harbin University of Science and Technology, 52 Xuefu Street, Harbin, 150080, Heilongjiang Province, China

    Chao Zhang, Weifeng Zhong & Haipeng Deng

  2. Changzhou Minseal Robot Technology Co., Ltd, 18 Wujin Middle Road, Changzhou, Jiangsu Province, China

    Chao Zhang & Changfeng Li

Authors
  1. Chao Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Weifeng Zhong
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Changfeng Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Haipeng Deng
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Chao Zhang is responsible for the entire experiment execution and paper writing. Weifeng Zhong is responsible for the review and correction of papers. Chengfeng Li and Haipeng Deng are responsible for guiding the experiment.

Corresponding author

Correspondence to Weifeng Zhong.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, C., Zhong, W., Li, C. et al. Random walk-based erasing data augmentation for deep learning. SIViP 17, 2447–2454 (2023). https://doi.org/10.1007/s11760-022-02461-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02461-3

Keywords