A survey on generative adversarial networks and their variants methods

Fatma Ben Aissa; Mahmoud Mejdoub; Mourad Zaied

doi:10.1117/12.2559848

31 January 2020 A survey on generative adversarial networks and their variants methods

Fatma Ben Aissa, Mahmoud Mejdoub, Mourad Zaied

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114333N (2020) https://doi.org/10.1117/12.2559848
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Data science becomes creative with generative adversarial networks (GANs) which have had a big success since they were introduced in 2014 by Ian J. Goodfellow and co-authors. In technical term the GANs are based on the unsupervised learning of two artificial neural networks called Generator and Discriminator both trained under the adversarial learning idea. The major goal of GAN is to generate new samples that estimate the potential distribution of real data. Due to its huge success, many modified versions have been proposed in the last two years. We summarize in this review paper GAN’s background, architecture and its application fields. Then, we discuss the different extensions of GAN over the original model and provide a comparative analysis of these techniques.

Citation Download Citation

Fatma Ben Aissa, Mahmoud Mejdoub, and Mourad Zaied "A survey on generative adversarial networks and their variants methods", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114333N (31 January 2020); https://doi.org/10.1117/12.2559848

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