Overview of Important GANs Models & Applications
Generative adversarial networks (GANs) have revolutionized image synthesis since their introduction in 2014. This article provides an overview of some of the most influential GAN models for key computer vision applications such as text-to-image generation, image-to-image translation, and image super-resolution.
GANs work by training two neural networks — a generator and a discriminator — in an adversarial game to output synthetic images that are indistinguishable from real images.
The first section describes StackGAN, a pioneering text-to-image GAN that uses a two-stage process to generate high-resolution, photo-realistic images from text descriptions.
Next, Pix2Pix GANs are examined for the general task of image-to-image translation across various domains. Pix2Pix introduced the idea of conditioning the generator on an input image to learn domain-specific loss functions.
The final section profiles SRGAN, which was developed specifically for super-resolving low-resolution images into high-definition versions.
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