A Primer on Generative Adversarial Networks
Journal Title: International Journal of Innovative Research in Computer Science and Technology - Year 2020, Vol 8, Issue 3
Abstract
Generative Adversarial Networks (GANs) is a type of deep neural network architecture that utilizes unsupervised machine learning to generate data. They were presented in 2014, in a paper by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This paper will introduce the core components of GANs. This will take you through how every part function and the significant ideas and innovation behind GANs. It will likewise give a short outline of the advantages and downsides of utilizing GANs, comparison of architectures of various GANs and knowledge into certain true applications.
Authors and Affiliations
Dr. Vikas Thada, Mr. Utpal Shrivastava, Jyotsna Sharma, Kuwar Prateek Singh, Manda Ranadeep
Keywords
Related Articles
- EP ID EP747817
- DOI 10.21276/ijircst.2020.8.3.22
- Views 43
- Downloads 0
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