CycleStyleGAN-based knowledge transfer for a machining digital twin

Zotov, E. and Kadirkamanathan, V. orcid.org/0000-0002-4243-2501 (2021) CycleStyleGAN-based knowledge transfer for a machining digital twin. Frontiers in Artificial Intelligence, 4. 767451.

Abstract

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Item Type: Article
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© 2021 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Keywords: knowledge transfer; transfer learning; domain adaptation; incremental learning; artificial intelligence; deep learning; generative adversarial network; industry 4.0
Dates:
  • Published: 25 November 2021
  • Published (online): 25 November 2021
  • Accepted: 26 October 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Funding Information:
Funder
Grant number
Engineering and Physical Sciences Research Council
EP/P006930/1
Depositing User: Symplectic Sheffield
Date Deposited: 14 Dec 2021 13:31
Last Modified: 14 Dec 2021 13:31
Status: Published
Publisher: Frontiers Media SA
Refereed: Yes
Identification Number: 10.3389/frai.2021.767451
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Open Archives Initiative ID (OAI ID):

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