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@INPROCEEDINGS{Kazimi:1033913,
      author       = {Kazimi, Bashir and Ruzaeva, Karina and Sandfeld, Stefan},
      title        = {{S}elf-{S}upervised {L}earning with {G}enerative
                      {A}dversarial {N}etworks for {E}lectron {M}icroscopy},
      publisher    = {IEEE},
      reportid     = {FZJ-2024-06751},
      pages        = {71-81},
      year         = {2024},
      abstract     = {In this work, we explore the potential of self-supervised
                      learning with Generative Adversarial Networks (GANs) for
                      electron microscopy datasets. We show how self-supervised
                      pretraining facilitates efficient fine-tuning for a spectrum
                      of downstream tasks, including semantic segmentation,
                      denoising, noise $\&$ background removal, and
                      super-resolution. Experimentation with varying model
                      complexities and receptive field sizes reveals the
                      remarkable phenomenon that fine-tuned models of lower
                      complexity consistently outperform more complex models with
                      random weight initialization. We demonstrate the versatility
                      of self-supervised pretraining across various downstream
                      tasks in the context of electron microscopy, allowing faster
                      convergence and better performance. We conclude that
                      self-supervised pretraining serves as a powerful catalyst,
                      being especially advantageous when limited annotated data
                      are available and efficient scaling of computational cost is
                      important.},
      month         = {Jun},
      date          = {2024-06-17},
      organization  = {2024 IEEE/CVF Conference on Computer
                       Vision and Pattern Recognition
                       Workshops (CVPRW), Seattle (WA), 17 Jun
                       2024 - 18 Jun 2024},
      cin          = {IAS-9},
      cid          = {I:(DE-Juel1)IAS-9-20201008},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:001327781700008},
      doi          = {10.1109/CVPRW63382.2024.00012},
      url          = {https://juser.fz-juelich.de/record/1033913},
}