Sequential Image Generation with GANs

Generative Adversarial Networks (GANs) are very successful in generating very sharp and realistic images. This post briefly explains our image generation framework based on GANs to sequentially compose an image scene, breaking down the underlying problem into smaller ones. For an in-depth description, please see our publication: A Layer-Based Sequential Framework for Scene Generation with GANs. A generative adversarial network (GAN) [1] is a class of machine learning frameworks. The generator takes a random noise as an input and generates a fake sample.