Unsupervised Representation Learning Based on Generative Adversarial Networks

  • Shi Xu
  • Jia WangEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1181)


This paper introduces a novel model for learning disentangled representations based on Generative Adversarial Networks. The training model is unsupervised without identity information. Unlike InfoGAN in which the disentangled representation is learnt by getting the variational lower bound of the mutual information indirectly, our method introduces a direct way by adding predicting networks and encoder into GANs and measuring the correlation among the encoder outputs. Experiment results on MNIST demonstrate that the proposed model is more generalizable and robust than InfoGAN. With experiments on Celeba-HQ, we show that our model can extract factorial features with complicate datasets and produce results comparable to supervised models.


Unsupervised representation learning Disentangled features Generative Adversarial Networks 



We thank the reviewers. This work is supported in part by the Chinese Science Foundation under grant 61771305.


  1. 1.
    Agakov, D.B.F.: The IM algorithm: a variational approach to information maximization. In: Advances in Neural Information Processing Systems, vol. 16, p. 201 (2004)CrossRefGoogle Scholar
  2. 2.
    Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223 (2017)Google Scholar
  3. 3.
    Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)CrossRefGoogle Scholar
  4. 4.
    Bengio, Y., et al.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Berthelot, D., Schumm, T., Metz, L.: Began: boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717 (2017)
  6. 6.
    Bourlard, H., Kamp, Y.: Auto-association by multilayer perceptrons and singular value decomposition. Biol. Cybern. 59(4–5), 291–294 (1988)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2172–2180 (2016)Google Scholar
  8. 8.
    Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning. arXiv preprint arXiv:1605.09782 (2016)
  9. 9.
    Endres, D.M., Schindelin, J.E.: A new metric for probability distributions. IEEE Trans. Inf. Theory 49, 1858–1860 (2003)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  11. 11.
    Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length and Helmholtz free energy. In: Advances in Neural Information Processing Systems, pp. 3–10 (1994)Google Scholar
  12. 12.
    Jolliffe, I.: Principal Component Analysis. Springer, New York (2011)zbMATHGoogle Scholar
  13. 13.
    Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)
  14. 14.
    Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)
  15. 15.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  16. 16.
    Liu, Y., Wei, F., Shao, J., Sheng, L., Yan, J., Wang, X.: Exploring disentangled feature representation beyond face identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2080–2089 (2018)Google Scholar
  17. 17.
    Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3730–3738 (2015)Google Scholar
  18. 18.
    Maaløe, L., Sønderby, C.K., Sønderby, S.K., Winther, O.: Improving semi-supervised learning with auxiliary deep generative models. In: NIPS Workshop on Advances in Approximate Bayesian Inference (2015)Google Scholar
  19. 19.
    MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, vol. 1, pp. 281–297 (1967)Google Scholar
  20. 20.
    Makhzani, A., Frey, B.: \(k\)-Sparse autoencoders. Comput. Sci. (2014)Google Scholar
  21. 21.
    Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders. arXiv preprint arXiv:1511.05644 (2015)
  22. 22.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  23. 23.
    Mitra, P., Murthy, C., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 301–312 (2002)CrossRefGoogle Scholar
  24. 24.
    Ng, A., et al.: Sparse autoencoder. In: CS294A Lecture Notes, vol. 72, pp. 1–19 (2011)Google Scholar
  25. 25.
    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)Google Scholar
  26. 26.
    Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103. ACM (2008)Google Scholar
  27. 27.
    Zhao, J., Mathieu, M., LeCun, Y.: Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126 (2016)

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina

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