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Review Article

JCR. 2020; 7(3): 753-758


Dr. Priyanka Chandani, Mr. Sanjay Kumar Nayak.

Generative adversarial network (GANs) is one of the foremost important research areas within the
field of AI, and its outstanding data generation capacity has received wide attention. During this paper, this paper
introduces the recent progress on GANs. First, the fundamental theory of Generative adversarial network (GAN)
and therefore, the differences among different generative models in recent years were analyzed and summarized.
Through an in-depth review of Generative adversarial network -related research within the literature, this paper
offers an account of the architecture-variants and loss-variants, which are proposed to handle these three
challenges from two perspectives. This paper presented loss-variants and architecture-variants for classifying the
foremost popular GANs and discuss the potential improvements by specializing in these two aspects. While
multiple reviews for GANs are presented to this point, none have focused on the review of Generative adversarial
network -variants supported their handling the challenges mentioned above. Additionally, in identifying different
methods for training and constructing Generative adversarial network, this paper also points to the remaining
challenges in their theory and application.

Key words: Generative Adversarial Network, Computer Vision, Neural Network, Unsupervised Learning

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