Skip to main content
Springer Nature Link
Log in

Learning Advisor Networks for Noisy Image Classification

  • Conference paper
  • First Online:
Image Analysis and Processing – ICIAP 2022 (ICIAP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13232))

Included in the following conference series:

Abstract

In this paper, we introduced the novel concept of advisor network to address the problem of noisy labels in image classification. Deep neural networks (DNN) are prone to performance reduction and overfitting problems on training data with noisy annotations. Weighting loss methods aim to mitigate the influence of noisy labels during the training, completely removing their contribution. This discarding process prevents DNNs from learning wrong associations between images and their correct labels but reduces the amount of data used, especially when most of the samples have noisy labels. Differently, our method weighs the feature extracted directly from the classifier without altering the loss value of each data. The advisor helps to focus only on some part of the information present in mislabeled examples, allowing the classifier to leverage that data as well. We trained it with a meta-learning strategy so that it can adapt throughout the training of the main model. We tested our method on CIFAR10 and CIFAR100 with synthetic noise, and on Clothing1M that contains real-world noise, reporting state-of-the-art results.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Algan, G., Ulusoy, I.: Image classification with deep learning in the presence of noisy labels: a survey. Knowl.-Based Syst. 215, 106771 (2021)

    Article  Google Scholar 

  2. Arazo, E., Ortego, D., Albert, P., O’Connor, N., McGuinness, K.: Unsupervised label noise modeling and loss correction. In: International Conference on Machine Learning, pp. 312–321. PMLR (2019)

    Google Scholar 

  3. Azadi, S., Feng, J., Jegelka, S., Darrell, T.: Auxiliary image regularization for deep CNNs with noisy labels. arXiv preprint arXiv:1511.07069 (2015)

  4. Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009)

    Google Scholar 

  5. Cheng, Y., Jiang, L., Macherey, W., Eisenstein, J.: AdvAug: robust adversarial augmentation for neural machine translation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5961–5970 (2020)

    Google Scholar 

  6. Goldberger, J., Ben-Reuven, E.: Training deep neural-networks using a noise adaptation layer (2016)

    Google Scholar 

  7. Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. In: Advances in Neural Information Processing Systems (2018)

    Google Scholar 

  8. Hendrycks, D., Mazeika, M., Wilson, D., Gimpel, K.: Using trusted data to train deep networks on labels corrupted by severe noise. In: Advances in Neural Information, vol. 31, pp. 10456–10465 (2018)

    Google Scholar 

  9. Jiang, L., Huang, D., Liu, M., Yang, W.: Beyond synthetic noise: deep learning on controlled noisy labels. In: International Conference on Machine Learning, pp. 4804–4815. PMLR (2020)

    Google Scholar 

  10. Jiang, L., Zhou, Z., Leung, T., Li, L.J., Fei-Fei, L.: MentorNet: learning data-driven curriculum for very deep neural networks on corrupted labels. In: International Conference on Machine Learning, pp. 2304–2313. PMLR (2018)

    Google Scholar 

  11. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)

    Google Scholar 

  12. Kumar, M., Packer, B., Koller, D.: Self-paced learning for latent variable models. In: Advances in Neural Information Processing Systems, vol. 23, pp. 1189–1197 (2010)

    Google Scholar 

  13. Li, J., Socher, R., Hoi, S.C.: DivideMix: learning with noisy labels as semi-supervised learning. In: International Conference on Learning Representations (2019)

    Google Scholar 

  14. Li, J., Wong, Y., Zhao, Q., Kankanhalli, M.S.: Learning to learn from noisy labeled data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5051–5059 (2019)

    Google Scholar 

  15. Li, Y., Yang, J., Song, Y., Cao, L., Luo, J., Li, L.J.: Learning from noisy labels with distillation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1910–1918 (2017)

    Google Scholar 

  16. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  17. Ma, X., et al.: Dimensionality-driven learning with noisy labels. In: International Conference on Machine Learning, pp. 3355–3364. PMLR (2018)

    Google Scholar 

  18. Nettleton, D.F., Orriols-Puig, A., Fornells, A.: A study of the effect of different types of noise on the precision of supervised learning techniques. Artif. Intell. Rev. 33(4), 275–306 (2010)

    Article  Google Scholar 

  19. Nishi, K., Ding, Y., Rich, A., Hollerer, T.: Augmentation strategies for learning with noisy labels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8022–8031 (2021)

    Google Scholar 

  20. Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., Qu, L.: Making deep neural networks robust to label noise: a loss correction approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1944–1952 (2017)

    Google Scholar 

  21. Pechenizkiy, M., Tsymbal, A., Puuronen, S., Pechenizkiy, O.: Class noise and supervised learning in medical domains: the effect of feature extraction. In: 19th IEEE Symposium on Computer-Based Medical Systems (CBMS 2006), pp. 708–713. IEEE (2006)

    Google Scholar 

  22. Reed, S., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., Rabinovich, A.: Training deep neural networks on noisy labels with bootstrapping. arXiv preprint arXiv:1412.6596 (2014)

  23. Ren, M., Zeng, W., Yang, B., Urtasun, R.: Learning to reweight examples for robust deep learning. In: International Conference on Machine Learning, pp. 4334–4343. PMLR (2018)

    Google Scholar 

  24. Shu, J., et al.: Meta-Weight-Net: learning an explicit mapping for sample weighting. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp. 1919–1930 (2019)

    Google Scholar 

  25. Tanaka, D., Ikami, D., Yamasaki, T., Aizawa, K.: Joint optimization framework for learning with noisy labels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5552–5560 (2018)

    Google Scholar 

  26. Vahdat, A.: Toward robustness against label noise in training deep discriminative neural networks. In: Advances in Neural Information Processing Systems, vol. 30, pp. 5596–5605 (2017)

    Google Scholar 

  27. Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., Belongie, S.: Learning from noisy large-scale datasets with minimal supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 839–847 (2017)

    Google Scholar 

  28. Wang, Z., Hu, G., Hu, Q.: Training noise-robust deep neural networks via meta-learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4524–4533 (2020)

    Google Scholar 

  29. 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)

    Google Scholar 

  30. Xiao, T., Xia, T., Yang, Y., Huang, C., Wang, X.: Learning from massive noisy labeled data for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2691–2699 (2015)

    Google Scholar 

  31. Xu, Y., Zhu, L., Jiang, L., Yang, Y.: Faster meta update strategy for noise-robust deep learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 144–153 (2021)

    Google Scholar 

  32. Yi, K., Wu, J.: Probabilistic end-to-end noise correction for learning with noisy labels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7017–7025 (2019)

    Google Scholar 

  33. Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. In: International Conference on Learning Representations (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Florence, Florence, Italy

    Simone Ricci, Tiberio Uricchio & Alberto Del Bimbo

Authors
  1. Simone Ricci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tiberio Uricchio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alberto Del Bimbo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tiberio Uricchio .

Editor information

Editors and Affiliations

  1. Boston University, Boston, MA, USA

    Stan Sclaroff

  2. National Research Council, Lecce, Italy

    Cosimo Distante

  3. National Research Council, Lecce, Italy

    Marco Leo

  4. University of Catania, Catania, Italy

    Giovanni M. Farinella

  5. Technische Universität München, Garching, Germany

    Federico Tombari

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ricci, S., Uricchio, T., Bimbo, A.D. (2022). Learning Advisor Networks for Noisy Image Classification. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06430-2_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06429-6

  • Online ISBN: 978-3-031-06430-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics