%0 Conference Paper
%A Kazimi, Bashir
%A Ruzaeva, Karina
%A Sandfeld, Stefan
%T Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy
%I IEEE
%M FZJ-2024-06751
%P 71-81
%D 2024
%X 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.
%B 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
%C 17 Jun 2024 - 18 Jun 2024, Seattle (WA)
Y2 17 Jun 2024 - 18 Jun 2024
M2 Seattle, WA
%F PUB:(DE-HGF)8
%9 Contribution to a conference proceedings
%U <Go to ISI:>//WOS:001327781700008
%R 10.1109/CVPRW63382.2024.00012
%U https://juser.fz-juelich.de/record/1033913