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@INPROCEEDINGS{Effenberger:905641,
      author       = {Effenberger, Frederic and Vasile, Ruggero and Cherti, Mehdi
                      and Kesselheim, Stefan and Jitsev, Jenia},
      title        = {{G}enerative {A}dversarial {D}eep {L}earning with {S}olar
                      {I}mages},
      reportid     = {FZJ-2022-00868},
      pages        = {1},
      year         = {2021},
      abstract     = {The Solar Dynamics Observatory (SDO) offers an
                      unprecedented, very large dataset of solar images in
                      different optical and EUV wavelength bands, capturing solar
                      atmospheric structures in high resolution and with excellent
                      coverage since 2010. This dataset is thus well suited to
                      study the application of advanced machine learning
                      techniques that require large amounts of data for training,
                      such as deep learning approaches. Here, we present results
                      of generative adversarial deep learning as applied to a
                      large database of solar images and discuss challenges in
                      training and validation, in particular with distributed
                      training on large computer clusters. We address the
                      potential of data augmentation techniques for improved
                      learning and image quality and the opportunities for latent
                      space structure exploration and control. Potential
                      application downstream that can make use of such generated
                      images are briefly discussed and the need for a
                      community-driven, physics-based basis to establish
                      evaluation criteria for generative models will be
                      emphasized.},
      month         = {Dec},
      date          = {2021-12-13},
      organization  = {AGU Fall Meeting, New Orleans (USA),
                       13 Dec 2021 - 17 Dec 2021},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / 5111 - Domain-Specific
                      Simulation $\&$ Data Life Cycle Labs (SDLs) and Research
                      Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)8},
      url          = {https://juser.fz-juelich.de/record/905641},
}