Artificial Intelligence

    

Generative Adversarial Networks

Authors: Amey Thakur, Mega Satish

Deep learning's breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results. The GAN allows the learning of deep representations in the absence of substantial labelled training information. Computer vision, language and video processing, and image synthesis are just a few of the applications that might benefit from these representations. The purpose of this research is to get the reader conversant with the GAN framework as well as to provide the background information on Generative Adversarial Networks, including the structure of both the generator and discriminator, as well as the various GAN variants along with their respective architectures. Applications of GANs are also discussed with examples.

Comments: 19 pages, 23 figures, Volume 9, Issue VIII, International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2021. DOI: https://doi.org/10.22214/ijraset.2021.37723

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[v1] 2021-08-31 12:44:04

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