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Single-task Temporal Face Synthesis Model Based on Migration Confrontation

  • Linlin Tang*
  • , Ruipei Sun
  • , Shiyu Qin
  • , Xing Huang
  • , Yijie Fan
  • , Minghua Hou
  • *Corresponding author for this work
  • Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Quality of face images generated by existing methods is not high and there is a lack of research on Asian face datasets. For synthesizing face images in a specific age domain, single-task temporal face synthesis model based on migration confrontation is proposed here. Transfer learning is used to redesign the network structure of the generated confrontation network and network structure of the discriminant network. And we also improve the loss function, so that model can better obtain feature information in a small amount of data set in a single-task scenario. Through experimental analysis, the model proposed in this paper performs better under objective evaluation indicators than existing models, and the model is more scalable and diverse.

Original languageEnglish
Title of host publicationProceedings of 2021 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021
EditorsDeyi Li, Mengqi Zhou, Weining Wang, Yaru Zou, Meng Luo, Qian Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages208-212
Number of pages5
ISBN (Electronic)9781665441490
DOIs
StatePublished - 2021
Externally publishedYes
Event7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021 - Xi'an, China
Duration: 7 Nov 20218 Nov 2021

Publication series

NameProceedings of 2021 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021

Conference

Conference7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021
Country/TerritoryChina
CityXi'an
Period7/11/218/11/21

Keywords

  • Generative Adversarial Network
  • Temporal Face Synthesis
  • Transfer Learning

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