TY  - CONF
AU  - Kazimi, Bashir
AU  - Ruzaeva, Karina
AU  - Sandfeld, Stefan
TI  - Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy
PB  - IEEE
M1  - FZJ-2024-06751
SP  - 71-81
PY  - 2024
AB  - 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.
T2  - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
CY  - 17 Jun 2024 - 18 Jun 2024, Seattle (WA)
Y2  - 17 Jun 2024 - 18 Jun 2024
M2  - Seattle, WA
LB  - PUB:(DE-HGF)8
UR  - <Go to ISI:>//WOS:001327781700008
DO  - DOI:10.1109/CVPRW63382.2024.00012
UR  - https://juser.fz-juelich.de/record/1033913
ER  -