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| 001 | 1033913 | ||
| 005 | 20250310131247.0 | ||
| 024 | 7 | _ | |a 10.1109/CVPRW63382.2024.00012 |2 doi |
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| 037 | _ | _ | |a FZJ-2024-06751 |
| 100 | 1 | _ | |a Kazimi, Bashir |0 P:(DE-Juel1)196697 |b 0 |
| 111 | 2 | _ | |a 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |c Seattle |d 2024-06-17 - 2024-06-18 |w WA |
| 245 | _ | _ | |a Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy |
| 260 | _ | _ | |c 2024 |b IEEE |
| 300 | _ | _ | |a 71-81 |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Ruzaeva, Karina |0 P:(DE-Juel1)180323 |b 1 |u fzj |
| 700 | 1 | _ | |a Sandfeld, Stefan |0 P:(DE-Juel1)186075 |b 2 |e Corresponding author |
| 773 | _ | _ | |a 10.1109/CVPRW63382.2024.00012 |
| 856 | 4 | _ | |u https://ieeexplore.ieee.org/document/10678518/authors#authors |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1033913/files/2402.18286v2.pdf |y OpenAccess |
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