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@INPROCEEDINGS{Sedona:890968,
      author       = {Sedona, Rocco and Cavallaro, Gabriele and Jitsev, Jenia and
                      Strube, Alexandre and Riedel, Morris and Book, Matthias},
      title        = {{S}caling {U}p a {M}ultispectral {R}esnet-50 to 128 {GPU}s},
      publisher    = {IEEE},
      reportid     = {FZJ-2021-01284},
      pages        = {1058 - 1061},
      year         = {2020},
      abstract     = {Similarly to other scientific domains, Deep Learning
                      (DL)holds great promises to fulfil the challenging needs of
                      RemoteSensing (RS) applications. However, the increase in
                      volume,variety and complexity of acquisitions that are
                      carried outon a daily basis by Earth Observation (EO)
                      missions generatesnew processing and storage challenges
                      within operationalprocessing pipelines. The aim of this work
                      is to show thatHigh-Performance Computing (HPC) systems can
                      speed upthe training time of Convolutional Neural Networks
                      (CNNs).Particular attention is put on the monitoring of the
                      classificationaccuracy that usually degrades when using
                      large batchsizes. The experimental results of this work show
                      that thetraining of the model scales up to a batch size of
                      8,000, obtainingclassification performances in terms of
                      accuracy in linewith those using smaller batch sizes.},
      month         = {Sep},
      date          = {2020-09-26},
      organization  = {2020 IEEE International Geoscience and
                       Remote Sensing Symposium, Online event
                       (Hawaii), 26 Sep 2020 - 2 Oct 2020},
      cin          = {JSC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / PhD no Grant - Doktorand ohne besondere
                      Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-512 / G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:000664335301039},
      doi          = {10.1109/IGARSS39084.2020.9324237},
      url          = {https://juser.fz-juelich.de/record/890968},
}