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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 (ICASSP), 15–20 Apr 2018, Calgary, Alberta, Canada.

Synthesis of images by two-stage generative adversarial networks.pdf - Accepted version Manuscript

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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 :
Plumbley, Mark
Date : 13 September 2018
DOI : 10.1109/ICASSP.2018.8461984
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 : 10 Dec 2018 14:32

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