IMAGE GENERATION USING GENERATIVE ADVERSARIAL NETWORKS

IMAGE GENERATION USING GENERATIVE ADVERSARIAL NETWORKS
Author(s):
ANAND UPADHYAY, SHIVPRAKASH VISHWAKARMA
Keywords:
Deep Learning, Generator, Discriminator, Gradient Descent, Image processing, CNN.
Abstract
In recent years, generative adversarial network have shown promising results in generating high-quality images in various domains. This paper present Methods for generating images using generative adversarial network to generate high quality images. DCGAN (Deep Convolutional Generative Adversarial Network) is a type of Generative Adversarial Network (GAN) used for image generation. The DCGAN architecture consists of two main components : a generator and network and a discriminator network. During training, the generator and discriminator are optimized simultaneously using backpropagation and stochastic gradient descent. The training process continues until the generator produces images that are indistinguishable from real images, as determined by the discriminator. DCGANs have been successful in generating high-quality images in various domains, including faces, objects, and scenes, and have become a popular choice for image generation tasks in machine learning.
Article Details
Unique Paper ID: 158567 Publication Volume & Issue: Volume 9, Issue 10 Page(s): 107 - 111
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