On the Tradeoff between Computation-Time and Learning-Accuracy in GAN-based Super-Resolution Deep Learning

Joo Yong Shim, Joongheon Kim, Jong Kook Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The trade-off between accuracy and computation should be considered when applying generative adversarial network (GAN)-based image generation to real-world applications. This paper presents a simple yet efficient method based on Progressive Growing of GANs (PGGAN) to exploit the trade-off for image generation. The scheme is evaluated using the LSUN dataset.

Original languageEnglish
Title of host publication35th International Conference on Information Networking, ICOIN 2021
PublisherIEEE Computer Society
Pages422-424
Number of pages3
ISBN (Electronic)9781728191003
DOIs
Publication statusPublished - 2021 Jan 13
Event35th International Conference on Information Networking, ICOIN 2021 - Jeju Island, Korea, Republic of
Duration: 2021 Jan 132021 Jan 16

Publication series

NameInternational Conference on Information Networking
Volume2021-January
ISSN (Print)1976-7684

Conference

Conference35th International Conference on Information Networking, ICOIN 2021
CountryKorea, Republic of
CityJeju Island
Period21/1/1321/1/16

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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