Generative Adversarial Networks: An Overview

التفاصيل البيبلوغرافية
العنوان: Generative Adversarial Networks: An Overview
المؤلفون: Creswell, Antonia, White, Tom, Dumoulin, Vincent, Arulkumaran, Kai, Sengupta, Biswa, Bharath, Anil A
سنة النشر: 2017
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition
الوصف: Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. The aim of this review paper is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.
Comment: Accepted in the IEEE Signal Processing Magazine Special Issue on Deep Learning for Visual Understanding
نوع الوثيقة: Working Paper
DOI: 10.1109/MSP.2017.2765202
URL الوصول: http://arxiv.org/abs/1710.07035
رقم الانضمام: edsarx.1710.07035
قاعدة البيانات: arXiv
الوصف
DOI:10.1109/MSP.2017.2765202