Generative Adversarial Networks: recent developments

2019/03/16 by Zamorski, Maciej, Zdobylak, Adrian, Zięba, Maciej +1
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · doi:10.48550/arxiv.1903.12266

Abstract

In traditional generative modeling, good data representation is very often a base for a good machine learning model. It can be linked to good representations encoding more explanatory factors that are hidden in the original data. With the invention of Generative Adversarial Networks (GANs), a subclass of generative models that are able to learn representations in an unsupervised and semi-supervised fashion, we are now able to adversarially learn good mappings from a simple prior distribution to a target data distribution. This paper presents an overview of recent developments in GANs with a focus on learning latent space representations.

Related

• Generative adversarial networks
• Adversarial Feature Learning
• Generative Adversarial Networks: An Overview
• Latent Space Conditioning on Generative Adversarial Networks
• GANspection