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@INPROCEEDINGS{Aach:910149,
      author       = {Aach, Marcel and Sedona, Rocco and Lintermann, Andreas and
                      Cavallaro, Gabriele and Neukirchen, Helmut and Riedel,
                      Morris},
      title        = {{A}ccelerating {H}yperparameter {T}uning of a {D}eep
                      {L}earning {M}odel for {R}emote {S}ensing {I}mage
                      {C}lassification},
      publisher    = {IEEE},
      reportid     = {FZJ-2022-03638},
      pages        = {263-266},
      year         = {2022},
      abstract     = {Deep Learning models have proven necessary in dealing with
                      the challenges posed by the continuous growth of data volume
                      acquired from satellites and the increasing complexity of
                      new Remote Sensing applications. To obtain the best
                      performance from such models, it is necessary to fine-tune
                      their hyperparameters. Since the models might have massive
                      amounts of parameters that need to be tuned, this process
                      requires many computational resources. In this work, a
                      method to accelerate hyperparameter optimization on a
                      High-Performance Computing system is proposed. The data
                      batch size is increased during the training, leading to a
                      more efficient execution on Graphics Processing Units. The
                      experimental results confirm that this method reduces the
                      runtime of the hyperparameter optimization step by a factor
                      of 3 while achieving the same validation accuracy as a
                      standard training procedure with a fixed batch size.},
      month         = {Jul},
      date          = {2022-07-17},
      organization  = {2022 IEEE International Geoscience and
                       Remote Sensing Symposium, Kuala Lumpur
                       (Malaysia), 17 Jul 2022 - 22 Jul 2022},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / RAISE - Research on
                      AI- and Simulation-Based Engineering at Exascale (951733) /
                      PhD no Grant - Doktorand ohne besondere Förderung
                      (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)951733 /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:000920916600066},
      doi          = {10.1109/IGARSS46834.2022.9883257},
      url          = {https://juser.fz-juelich.de/record/910149},
}