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Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles

Kamilaris Andreas; van den Brink Corjan; Karatsiolis Savvas

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@inproceedings{kamilaris_andreas_2019_3523006,
  author       = {Kamilaris Andreas and
                  van den Brink Corjan and
                  Karatsiolis Savvas},
  title        = {{Training Deep Learning Models via Synthetic Data: 
                   Application in Unmanned Aerial Vehicles}},
  booktitle    = {{Computer Analysis of Images and Patterns. CAIP 
                   2019. Communications in Computer and Information
                   Science, vol 1089}},
  year         = 2019,
  pages        = {81-90},
  publisher    = {Springer International Publishing},
  address      = {Cham},
  month        = oct,
  note         = {{This work has received funding from the European 
                   Union's Horizon 2020 Research and Innovation
                   Programme under Grant Agreement  No 739578 and the
                   Government of the Republic of Cyprus through the
                   Directorate General for European Programmes,
                   Coordination and Development.}},
  doi          = {10.5281/zenodo.3523006},
  url          = {https://doi.org/10.5281/zenodo.3523006}
}
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Publication date:
October 30, 2019
DOI:
10.5281/zenodo.3523006

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Keyword(s):
UAV Deep Learning Generative Data Aerial Imagery
Published in:
Computer Analysis of Images and Patterns. CAIP 2019. Communications in Computer and Information Science, vol 1089, Springer International Publishing, Cham, pp. 81-90.
Grants:
European Commission:
  • RISE - Research Center on Interactive Media, Smart System and Emerging Technologies (739578)
Communities:
License (for files):
Creative Commons Attribution Non Commercial No Derivatives 4.0 International

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