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Meta-styled CNNs: boosting robustness through adaptive learning and style transfer

  • Jugl

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
JournalInternational Journal of Information Technology (Singapore)
DOIs
StateAccepted/In press - 2024
Externally publishedYes

ASJC Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics
  • Electrical and Electronic Engineering

Keywords

  • Convolutional neural networks
  • Robustness
  • Style transfer
  • Training Data

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