University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Synthesis of images by two-stage generative adversarial networks

Huang, Qiang, Jackson, Philip, Plumbley, Mark D. and Wang, Wenwu (2018) Synthesis of images by two-stage generative adversarial networks In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, 15–20 Apr 2018, Calgary, Alberta, Canada.

[img]
Preview
Text
Synthesis of images by two-stage generative adversarial networks.pdf - Accepted version Manuscript

Download (942kB) | Preview

Abstract

In this paper, we propose a divide-and-conquer approach using two generative adversarial networks (GANs) to explore how a machine can draw colorful pictures (bird) using a small amount of training data. In our work, we simulate the procedure of an artist drawing a picture, where one begins with drawing objects’ contours and edges and then paints them different colors. We adopt two GAN models to process basic visual features including shape, texture and color. We use the first GAN model to generate object shape, and then paint the black and white image based on the knowledge learned using the second GAN model. We run our experiments on 600 color images. The experimental results show that the use of our approach can generate good quality synthetic images, comparable to real ones.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Huang, Qiangq.huang@surrey.ac.uk
Jackson, PhilipP.Jackson@surrey.ac.uk
Plumbley, Mark D.m.plumbley@surrey.ac.uk
Wang, WenwuW.Wang@surrey.ac.uk
Date : 2018
Copyright Disclaimer : Copyright © 2018, IEEE
Uncontrolled Keywords : Generative adversarial networks; Conditional, Image generation
Related URLs :
Depositing User : Clive Harris
Date Deposited : 14 Mar 2018 11:32
Last Modified : 18 Apr 2018 10:23
URI: http://epubs.surrey.ac.uk/id/eprint/846022

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800