Skip to main content
SpringerLink
Log in

Semi-supervised learning using adversarial training with good and bad samples

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training. Previous results have illustrated that generative adversarial networks (GANs) can be used for multiple purposes in SSL . Triple-GAN, which aims to jointly optimize model components by incorporating three players, generates suitable image-label pairs to compensate for the lack of labeled data in SSL with improved benchmark performance. Conversely, Bad (or complementary) GAN optimizes generation to produce complementary data-label pairs and force a classifier’s decision boundary to lie between data manifolds. Although it generally outperforms Triple-GAN, Bad GAN is highly sensitive to the amount of labeled data used for training. Unifying these two approaches, we present unified-GAN (UGAN), a novel framework that enables a classifier to simultaneously learn from both good and bad samples through adversarial training. We perform extensive experiments on various datasets and demonstrate that UGAN: (1) achieves competitive performance among other GAN-based models, and (2) is robust to variations in the amount of labeled data used for training.

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

Access this article

Log in via an institution

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

Notes

  1. In semi-supervised learning, p(x) is the empirical distribution of inputs and p(y) is assumed same to the distribution of labels on labeled data, which is uniform in our experiments.

  2. In practice, we use \(L_{gG} = -\mathbb {E}_{x, y\sim p_{gG}(x, y)}[\log (p_{D}(x, y)]\) to ease the training process [9].

References

  1. Abu-El-Haija, S., Kothari, N., Lee, J., Natsev, P., Toderici, G., Varadarajan, B., Vijayanarasimhan, S.: Youtube-8m: A Large-scale Video Classification Benchmark (2016). arXiv:1609.08675

  2. Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006)

    MathSciNet  MATH  Google Scholar 

  3. Brock, A., Donahue, J., Simonyan, K.: Large Scale Gan Training For High Fidelity Natural Image Synthesis. (2018). arXiv:1809.11096

  4. Dai, Z., Yang, Z., Yang, F., Cohen, W.W., Salakhutdinov, R.R.: Good semi-supervised learning that requires a bad gan. In: Advances in Neural Information Processing Systems, pp. 6510–6520 (2017)

  5. 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)

  6. Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., Courville, A.: Adversarially learned inference (2016). arXiv:1606.00704

  7. Gan, Z., Chen, L., Wang, W., Pu, Y., Zhang, Y., Liu, H., Li, C., Carin, L.: Triangle generative adversarial networks. In: Advances in Neural Information Processing Systems, pp. 5247–5256 (2017)

  8. Girija, S.S.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. Software available from https://www.tensorflow.org/ (2016)

  9. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

  10. Iscen, A., Tolias, G., Avrithis, Y., Chum, O.: Label propagation for deep semi-supervised learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5070–5079 (2019)

  11. Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 (2014)

  12. Kingma, D.P., Welling, M.: Auto-Encoding Variational Bayes. arXiv:1312.6114 (2013)

  13. Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-El-Haija, S., Kuznetsova, A., Rom, H., Uijlings, J., Popov, S., Veit, A et al.: Openimages: A Public Dataset for Large-scale Multi-label and Multi-class Image Classification, vol. 2, p. 3. Dataset available from https://github.com/openimages (2017)

  14. Krizhevsky, A., Hinton, G.: Learning Multiple Layers of Features from Tiny Images. Technical report, Citeseer (2009)

  15. Kumar, A., Sattigeri, P., Fletcher, T.: Semi-supervised Learning with Gans: Manifold Invariance with Improved Inference. In: Advances in Neural Information Processing Systems, pp. 5534–5544 (2017)

  16. Laine, S., Aila, T.: Temporal Ensembling for Semi-supervised Learning. arXiv:1610.02242 (2016)

  17. Lecouat, B., Foo, C.-S., Zenati, H., Chandrasekhar, V.R.: Semi-supervised Learning with Gans: Revisiting Manifold Regularization. arXiv:1805.08957 (2018)

  18. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  19. Li, C., Xu, T., Zhu, J., Zhang, B.: Triple generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 4088–4098 (2017)

  20. Li, W., Wang, Y., Cai, Y., Arnold, C., Zhao, E., Yuan, Y.: Semi-supervised Rare Disease Detection Using Generative Adversarial Network. arXiv:1812.00547 (2018)

  21. Li, W., Wang, Z., Li, J., Polson, J., Speier, W., Arnold, C.: Semi-supervised Learning Based on Generative Adversarial Network: A Comparison Between Good Gan and Bad Gan Approach. arXiv:1905.06484 (2019)

  22. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: common objects in context. In: European Conference on Computer Vision, pp. 740–755. Springer (2014)

  23. Lucic, M., Tschannen, M., Ritter, M., Zhai, X., Bachem, O., Gelly, S.: High-Fidelity Image Generation with Fewer Labels. arXiv:1903.02271 (2019)

  24. Mirza, M., Osindero, S.: Conditional Generative Adversarial Nets. arXiv:1411.1784 (2014)

  25. Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral Normalization for Generative Adversarial Networks. arXiv:1802.05957 (2018)

  26. Miyato, T., Maeda, S.I., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1979–1993 (2018)

    Article  Google Scholar 

  27. Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: Advances in Neural Information Processing Systems (2011)

  28. Nigam, K., McCallum, A., Mitchell, T.: Semi-supervised text classification using EM. In: Semi-supervised Learning, pp. 33–56 (2006)

  29. Pu, Y., Gan, Z., Henao, R., Yuan, X., Li, C., Stevens, A., Carin, L.: Variational autoencoder for deep learning of images, labels and captions. In: Advances in Neural Information Processing Systems, pp. 2352–2360 (2016)

  30. Qiao, S., Shen, W., Zhang, Z., Wang, B., Yuille, A.: Deep co-training for semi-supervised image recognition. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 135–152 (2018)

  31. Radford, A., Metz, L., Chintala, S.: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv:1511.06434 (2015)

  32. Rasmus, A., Berglund, M., Honkala, M., Valpola, H., Raiko, T.; Semi-supervised Learning with Ladder Networks. In: Advances in Neural Information Processing Systems, pp. 3546–3554 (2015)

  33. Sajjadi, M., Javanmardi, M., Tasdizen, T.: Mutual exclusivity loss for semi-supervised deep learning. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 1908–1912. IEEE (2016)

  34. Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. In: Advances in Neural Information Processing Systems, pp. 2234–2242 (2016)

  35. Springenberg, J.T.: Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks. arXiv:1511.06390 (2015)

  36. Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, pp. 1195–1204 (2017)

  37. Weston, J., Ratle, F., Mobahi, H., Collobert, R.: Deep learning via semi-supervised embedding. In: Neural Networks: Tricks of the Trade, pp. 639–655. Springer (2012)

Download references

Author information

Authors and Affiliations

  1. University of California, Los Angeles, USA

    Wenyuan Li, Zichen Wang, Jiayun Li, William Speier & Corey Arnold

  2. University of Texas, Austin, USA

    Yuguang Yue & Mingyuan Zhou

Authors
  1. Wenyuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zichen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuguang Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiayun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. William Speier
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mingyuan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Corey Arnold
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Wenyuan Li or Corey Arnold.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 2355 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, W., Wang, Z., Yue, Y. et al. Semi-supervised learning using adversarial training with good and bad samples. Machine Vision and Applications 31, 49 (2020). https://doi.org/10.1007/s00138-020-01096-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-020-01096-z

Keywords

Search

Navigation