Generative Adversarial Networks

This paper provides a comprehensive guide to Generative Adversarial Networks (GANs), a class of deep learning techniques noted for generating realistic images, videos, and other data types. Rather than presenting new experimental results, its method is expository: it introduces GANs and their historical development, reviews background and related work, then details the generator-and-discriminator architecture along with key design choices and variations. It further surveys the loss functions used in GANs, from the original minimax objective to more recent approaches like Wasserstein distance with gradient penalty, and reviews training techniques such as alternating optimization, minibatch discrimination, and spectral normalization.

The guide also surveys GAN applications across domains, the evaluation metrics used to judge the diversity and quality of generated data, and open challenges such as mode collapse, training instability, and ethical considerations, before outlining future directions including scalability, new architectures, and incorporating domain knowledge. This mattered as a consolidated theoretical and practical reference, intended to orient researchers and practitioners across the full landscape of GAN methods, applications, and open problems in one place.