Building a Generative Adversarial Network for Image Synthesis

Building a Generative Adversarial Network for Image Synthesis

B. Yashas Chandra

Graduate Student

Department of Information Technology Malla Reddy College of Engineering & Technology Hyderabad, India.

yashas144@gmail.com

Abstract
Generative Adversarial Networks (GANs) have emerged as a powerful class of generative models, capable of synthesizing realistic images by leveraging adversarial training. It explores the process of building a Generative Adversarial Network for image synthesis, delving into the underlying architecture, training methodology, and potential applications. Generative Adversarial Networks typically run unsupervised and use a cooperative zero-sum game framework to learn, where one person's gain equals another person's loss.

The proposed Generative Adversarial Network architecture consists of a generator network that learns to create images from random noise and a discriminator network trained to distinguish between real and generated images. Through an adversarial training process, these networks iteratively refine their capabilities, resulting in a generator that produces increasingly realistic pictures and a discriminator with enhanced discriminative abilities. Generative Adversarial Networks are an effective tool for producing realistic, high-quality outputs in a variety of fields, including text and image generation, because of this back-and-forth competition, which results in the creation of increasingly convincing and indistinguishable synthetic data.