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@INPROCEEDINGS{Zhang:890969,
      author       = {Zhang, Run and Cavallaro, Gabriele and Jitsev, Jenia},
      title        = {{S}uper-{R}esolution of {L}arge {V}olumes of {S}entinel-2
                      {I}mages with {H}igh {P}erformance {D}istributed {D}eep
                      {L}earning},
      reportid     = {FZJ-2021-01285},
      pages        = {617 - 620},
      year         = {2020},
      abstract     = {This work proposes a novel distributed deep learning
                      modelfor Remote Sensing (RS) images super-resolution. High
                      PerformanceComputing (HPC) systems with GPUs are used
                      toaccelerate the learning of the unknown low to high
                      resolutionmapping from large volumes of Sentinel-2 data. The
                      proposeddeep learning model is based on self-attention
                      mechanismand residual learning. The results demonstrate that
                      stateof-the-art performance can be achieved by keeping the
                      size ofthe model relatively small. Synchronous data
                      parallelism isapplied to scale up the training process
                      without severe performanceloss. Distributed training is thus
                      shown to speed uplearning substantially while keeping
                      performance intact.},
      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) / HBP - The Human Brain Project (604102)},
      pid          = {G:(DE-HGF)POF3-512 / G:(EU-Grant)604102},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:000664335300139},
      doi          = {10.1109/IGARSS39084.2020.9323734},
      url          = {https://juser.fz-juelich.de/record/890969},
}