Generative Adversarial Networks: recent developments

Autor:	Zamorski, Maciej, Zdobylak, Adrian, Zięba, Maciej, Świątek, Jerzy
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1903.12266 Zobrazit plný text záznamu View this record from Arxiv