Unsupervised Learning of Image Data Using Generative Adversarial Network

  • Rayner AlfredEmail author
  • Chew Ye Lun
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1041)


Over the past few years, with the introduction of deep learning techniques such as convolution neural network (CNN), supervised learning with CNN had achieved a huge success in the computer vision area such as classifying digital images. However, supervised learning has a major drawback, in which it requires a large dataset for them to perform more effectively. As the data used in training grew bigger, the cost of labeling data for training becomes more expensive and impractical. In order to resolve this issue, unsupervised learning is encouraged to be used as it can draw inferences from datasets consisting of unlabeled input data. Generative adversarial network (GAN) is one of the unsupervised learning technique that has the ability to create natural-looking images, converting text description into images, recover resolution of images and last but not least, its power of representation learning from unlabeled data. Thus, this study attempts to evaluate the effectiveness of GAN algorithm in performing the supervised task and unsupervised task such as classification and clustering. Based on the results obtained, the GAN algorithm can learn the internal representation of data without labels and can act as good features extractor. Future works include applying GAN framework in other domains such as video, natural language processing and text to image synthesis.


Unsupervised learning Supervised learning Generative adversarial network Feature extraction 


  1. 1.
    O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A.C. Berg, ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    E.A. Hay, R. Parthasarathy, Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets. PLoS Comput. Biol. 14(12), e1006628 (2018). Scholar
  3. 3.
    W. Rawat, Z. Wang, Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 29, 2352–2449 (2017)MathSciNetCrossRefGoogle Scholar
  4. 4.
    I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets. Adv. Neural Inf. Process. Syst. 2672–2680 (2014)Google Scholar
  5. 5.
    V. Premachandran, A.L. Yuille, Unsupervised learning using generative adversarial training and clustering, in Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France, 24–26 April 2017Google Scholar
  6. 6.
    X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, P. Abbeel, InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. Adv. Neural Inf. Process. Syst. 2172–2180 (2016)Google Scholar
  7. 7.
    X. Mao, et al., Least squares generative adversarial networks, in 2017 IEEE International Conference on Computer Vision (ICCV) (IEEE, 2017)Google Scholar
  8. 8.
    J.T. Springenberg, Unsupervised and semi-supervised learning with categorical generative adversarial networks. arXiv preprint arXiv:1511.06390 (2016)
  9. 9.
    M. Arjovsky, S. Chintala, L. Bottou, Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017)
  10. 10.
    A. Coates, H. Lee, A.Y. Ng, An analysis of single layer networks in unsupervised feature learning, in AISTATS, 2011Google Scholar
  11. 11.
    Y. Koshiba, S. Abe, Comparison of L1 and L2 support vector machines, in Proceedings of the International Joint Conference on Neural Networks, 2003, vol. 3 (IEEE, 2003), pp. 2054–2059Google Scholar
  12. 12.
    Y. Tang, Deep learning using support vector machines. CoRR, abs/1306.0239, 2013Google Scholar
  13. 13.
    T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, Improved techniques for training GANs, in Advances in Neural Information Processing Systems (2016), pp. 2234–2242Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Knowledge Technology Research Unit, Faculty of Computing and InformaticsUniversiti Malaysia SabahKota KinabaluMalaysia

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