Skip to main content
SpringerLink
Log in
Book cover

Deep Neural Evolution pp 293–322Cite as

Neuroevolution of Generative Adversarial Networks

  • Chapter
  • First Online:

Part of the book series: Natural Computing Series ((NCS))

Abstract

Generative Adversarial Networks (GAN) is an adversarial model that became relevant in the last years, displaying impressive results in generative tasks. A GAN combines two neural networks, a discriminator and a generator, trained in an adversarial way. The discriminator learns to distinguish between real samples of an input dataset and fake samples. The generator creates fake samples aiming to fool the discriminator. The training progresses iteratively, leading to the production of realistic samples that can mislead the discriminator. Despite the impressive results, GANs are hard to train, and a trial-and-error approach is generally used to obtain consistent results. Since the original GAN proposal, research has been conducted not only to improve the quality of the generated results but also to overcome the training issues and provide a robust training process. However, even with the advances in the GAN model, stability issues are still present in the training of GANs. Neuroevolution, the application of evolutionary algorithms in neural networks, was recently proposed as a strategy to train and evolve GANs. These proposals use the evolutionary pressure to guide the training of GANs to build robust models, leveraging the quality of results, and providing a more stable training. Furthermore, these proposals can automatically provide useful architectural definitions, avoiding the manual discovery of suitable models for GANs. We show the current advances in the use of evolutionary algorithms and GANs, presenting the state-of-the-art proposals related to this context. Finally, we discuss perspectives and possible directions for further advances in the use of evolutionary algorithms and GANs.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    A list of proposals related to GANs can be found at https://github.com/hindupuravinash/the-gan-zoo.

  2. 2.

    Code available at https://github.com/WANG-Chaoyue/EvolutionaryGAN.

  3. 3.

    Code available at https://github.com/unaigarciarena/GAN_Evolution.

  4. 4.

    Code available at https://github.com/ALFA-group/lipizzaner-gan.

  5. 5.

    Code available at https://github.com/mustang-gan/mustang.

  6. 6.

    Code available at https://github.com/vfcosta/coegan.

References

  1. Al-Dujaili, A., Schmiedlechner, T., Hemberg, E., O’Reilly, U.M.: Towards distributed coevolutionary GANs. In: AAAI 2018 Fall Symposium (2018)

    Google Scholar 

  2. Antonio, L.M., Coello, C.A.C.: Coevolutionary multiobjective evolutionary algorithms: survey of the state-of-the-art. IEEE Trans. Evol. Comput. 22(6), 851–865 (2018)

    Article  Google Scholar 

  3. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223 (2017)

    Google Scholar 

  4. Barratt, S., Sharma, R.: A note on the inception score. Preprint, arXiv:1801.01973 (2018)

    Google Scholar 

  5. Berthelot, D., Schumm, T., Metz, L.: BEGAN: Boundary equilibrium generative adversarial networks. Preprint, arXiv:1703.10717 (2017)

    Google Scholar 

  6. Borji, A.: Pros and cons of GAN evaluation measures. Comput. Vis. Image Underst. 179, 41–65 (2019)

    Article  Google Scholar 

  7. Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, 6–9 May 2019

    Google Scholar 

  8. Coello, C.A.C., Sierra, M.R.: A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm. In: Mexican International Conference on Artificial Intelligence, pp. 688–697. Springer, Berlin (2004)

    Google Scholar 

  9. Costa, V., Lourenço, N., Correia, J., Machado, P.: COEGAN: evaluating the coevolution effect in generative adversarial networks. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 374–382. ACM, New York (2019)

    Google Scholar 

  10. Costa, V., Lourenço, N., Machado, P.: Coevolution of generative adversarial networks. In: International Conference on the Applications of Evolutionary Computation (Part of EvoStar), pp. 473–487. Springer, Berlin (2019)

    Chapter  Google Scholar 

  11. Durugkar, I., Gemp, I., Mahadevan, S.: Generative multi-adversarial networks. Preprint, arXiv:1611.01673 (2016)

    Google Scholar 

  12. Elgammal, A., Liu, B., Elhoseiny, M., Mazzone, M.: CAN: Creative adversarial networks, generating “art” by learning about styles and deviating from style norms. Preprint, arXiv:1706.07068 (2017)

    Google Scholar 

  13. Fedus, W., Rosca, M., Lakshminarayanan, B., Dai, A.M., Mohamed, S., Goodfellow, I.: Many paths to equilibrium: GANs do not need to decrease a divergence at every step. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, 30 April–3 May 2018, Conference Track Proceedings (2018)

    Google Scholar 

  14. Garciarena, U., Santana, R., Mendiburu, A.: Evolved GANs for generating Pareto set approximations. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’18, pp. 434–441. ACM, New York (2018)

    Google Scholar 

  15. Ghosh, A., Kulharia, V., Namboodiri, V.P., Torr, P.H., Dokania, P.K.: Multi-agent diverse generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8513–8521 (2018)

    Google Scholar 

  16. 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)

    Google Scholar 

  17. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, pp. 5769–5779 (2017)

    Google Scholar 

  18. Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local NASH equilibrium. In: Advances in Neural Information Processing Systems, pp. 6629–6640 (2017)

    Google Scholar 

  19. Hillis, W.D.: Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D 42(1–3), 228–234 (1990)

    Article  Google Scholar 

  20. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)

    Google Scholar 

  21. Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, 30 April–3 May 2018, Conference Track Proceedings (2018)

    Google Scholar 

  22. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. Preprint, arXiv:1812.04948 (2018)

    Google Scholar 

  23. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, 7–9 May 2015, Conference Track Proceedings (2015)

    Google Scholar 

  24. LeCun, Y.: The MNIST database of handwritten digits. http://yann.lecun.com/exdb/mnist/ (1998)

  25. 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 

  26. Lucic, M., Kurach, K., Michalski, M., Gelly, S., Bousquet, O.: Are GANs created equal? A large-scale study. Preprint, arXiv:1711.10337 (2017)

    Google Scholar 

  27. Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Smolley, S.P.: Least squares generative adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2813–2821. IEEE, Piscataway (2017)

    Google Scholar 

  28. Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Navruzyan, A., Duffy, N., Hodjat, B.: Evolving deep neural networks. Preprint, arXiv:1703.00548 (2017)

    Google Scholar 

  29. Mirza, M., Osindero, S.: Conditional generative adversarial nets. Preprint, arXiv:1411.1784 (2014)

    Google Scholar 

  30. Mitchell, M.: Coevolutionary learning with spatially distributed populations. In: Computational Intelligence: Principles and Practice, pp. 137–154. IEEE Computational Intelligence Society, Piscataway (2006)

    Google Scholar 

  31. Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, 30 April–3 May 2018, Conference Track Proceedings (2018)

    Google Scholar 

  32. Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 2642–2651. JMLR.org (2017)

    Google Scholar 

  33. Olsson, C., Bhupatiraju, S., Brown, T., Odena, A., Goodfellow, I.: Skill rating for generative models. Preprint, arXiv:1808.04888 (2018)

    Google Scholar 

  34. Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., Zheng, Y.: Recent progress on generative adversarial networks (GANs): A survey. IEEE Access 7, 36322–36333 (2019)

    Article  Google Scholar 

  35. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. Preprint, arXiv:1511.06434 (2015)

    Google Scholar 

  36. Rawal, A., Rajagopalan, P., Miikkulainen, R.: Constructing competitive and cooperative agent behavior using coevolution. In: 2010 IEEE Symposium on Computational Intelligence and Games (CIG), pp. 107–114 (2010)

    Google Scholar 

  37. Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. Preprint, arXiv:1605.05396 (2016)

    Google Scholar 

  38. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  39. 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)

    Google Scholar 

  40. Schäfer, F., Anandkumar, A.: Competitive gradient descent. Preprint, arXiv:1905.12103 (2019)

    Google Scholar 

  41. Sims, K.: Evolving 3d morphology and behavior by competition. Artif. Life 1(4), 353–372 (1994)

    Article  Google Scholar 

  42. Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)

    Article  Google Scholar 

  43. Stanley, K.O., Miikkulainen, R.: Competitive coevolution through evolutionary complexification. J. Artif. Intell. Res. 21, 63–100 (2004)

    Article  Google Scholar 

  44. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  45. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)

    Google Scholar 

  46. Toutouh, J., Hemberg, E., O’Reilly, U.M.: Spatial evolutionary generative adversarial networks. Preprint, arXiv:1905.12702 (2019)

    Google Scholar 

  47. Wang, C., Xu, C., Yao, X., Tao, D.: Evolutionary generative adversarial networks. Preprint, arXiv:1803.00657 (2018)

    Google Scholar 

  48. Wang, Z., She, Q., Ward, T.E.: Generative adversarial networks: a survey and taxonomy. Preprint, arXiv:1906.01529 (2019)

    Google Scholar 

  49. Xu, Q., Huang, G., Yuan, Y., Guo, C., Sun, Y., Wu, F., Weinberger, K.: An empirical study on evaluation metrics of generative adversarial networks. Preprint, arXiv:1806.07755 (2018)

    Google Scholar 

  50. Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423–1447 (1999)

    Article  Google Scholar 

  51. Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. Preprint, arXiv:1805.08318 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Victor Costa, Nuno Lourenço, João Correia & Penousal Machado

Authors
  1. Victor Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nuno Lourenço
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. João Correia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Penousal Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Victor Costa .

Editor information

Editors and Affiliations

  1. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan

    Hitoshi Iba

  2. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Nasimul Noman

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Costa, V., Lourenço, N., Correia, J., Machado, P. (2020). Neuroevolution of Generative Adversarial Networks. In: Iba, H., Noman, N. (eds) Deep Neural Evolution. Natural Computing Series. Springer, Singapore. https://doi.org/10.1007/978-981-15-3685-4_11

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-3685-4_11

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3684-7

  • Online ISBN: 978-981-15-3685-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation