Abstract
Generative adversarial network (GAN) is a powerful method to reproduce the distribution of a given data set. It is widely used for generating photo-realistic images or data collections that appear real. Evolutionary GAN (E-GAN) is one of state-of-the-art GAN variations. E-GAN combines population based search and evolutionary operators from evolutionary algorithms with GAN to enhance diversity and search performance. In this study we aim to improve E-GAN by adding transfer learning and crossover which is a key evolutionary operator that is commonly used in evolutionary algorithms, but not in E-GAN.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862 (2017)
Arora, S., Zhang, Y.: Do GANs actually learn the distribution? An empirical study. arXiv: abs/1706.08224 (2017)
Arotaritei, D.: Genetic algorithm for fuzzy neural networks using locally crossover. Int. J. Comput. Commun. Control 6(1), 8–20 (2011)
Foo, Y.W., Goh, C., Lim, H.C., Zhan, Z.H., Li, Y.: Evolutionary neural network based energy consumption forecast for cloud computing. In: 2015 International Conference on Cloud Computing Research and Innovation (ICCCRI), pp. 53–64. IEEE (2015)
García-Pedrajas, N., Ortiz-Boyer, D., Hervás-Martínez, C.: An alternative approach for neural network evolution with a genetic algorithm: crossover by combinatorial optimization. Neural Netw. 19(4), 514–528 (2006)
Goodfellow, I.J., et al.: Generative adversarial networks. arXiv: abs/1406.2661 (2014)
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. arXiv preprint arXiv:1706.08500 (2017)
Kim, K.J., Cho, S.B.: Prediction of colon cancer using an evolutionary neural network. Neurocomputing 61, 361–379 (2004)
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
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)
Mo, S., Cho, M., Shin, J.: Freeze the discriminator: a simple baseline for fine-tuning GANs. arxiv 2020. arXiv preprint arXiv:2002.10964 (2020)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. arXiv preprint arXiv:1606.03498 (2016)
Spears, W.M., Anand, V.: A study of crossover operators in genetic programming. In: Ras, Z.W., Zemankova, M. (eds.) ISMIS 1991. LNCS, vol. 542, pp. 409–418. Springer, Heidelberg (1991). https://doi.org/10.1007/3-540-54563-8_104
Wang, C., Xu, C., Yao, X., Tao, D.: Evolutionary generative adversarial networks. IEEE Trans. Evol. Comput. 23, 921–934 (2019)
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., Xiao, J.: LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365 (2015)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, Z., Sabar, N., Song, A. (2022). Improving Evolutionary Generative Adversarial Networks. In: Long, G., Yu, X., Wang, S. (eds) AI 2021: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13151. Springer, Cham. https://doi.org/10.1007/978-3-030-97546-3_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-97546-3_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97545-6
Online ISBN: 978-3-030-97546-3
eBook Packages: Computer ScienceComputer Science (R0)