Generative AI

2Generative Adversarial Networks (GANs)

Ankur Chaudhary^*, Ritesh Rastogi, Aditee Mattoo, Punit Kumar, Tanvi Kumari and Devansh Dubey

Noida Institute of Engineering & Technology, Greater Noida, India

Abstract

Generative Adversarial Networks (GANs) have emerged as a revolutionary paradigm within the realms of machine learning and artificial intelligence, offering a fresh perspective on generative modelling. Originating from the collaborative efforts of Ian Goodfellow and his peers in 2014, GANs have demonstrated transformative potential across a multitude of domains.

In essence, GANs feature a dynamic interplay between two neural networks: the generator and discriminator. Engaged in a competitive interplay, the generator strives to produce synthetic data that mirrors the authenticity of real data, whereas the discriminator works to distinguish between authentic and generated data. This adversarial training process propels both networks to continuously refine their capabilities and establish a dynamic equilibrium.

The efficacy of GANs stems from their ability to comprehend and reproduce the intricate patterns inherent in complex datasets. This proficiency has profound applications in image generation, where GANs have shown unparalleled skills in crafting realistic, high-resolution images, such as the generation of lifelike faces, landscapes, and artworks, underscore the aesthetic potential of GANs, transcending the limitations associated with traditional generative models.