Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy
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.
Cite
Text
Kazimi et al. "Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00012Markdown
[Kazimi et al. "Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/kazimi2024cvprw-selfsupervised/) doi:10.1109/CVPRW63382.2024.00012BibTeX
@inproceedings{kazimi2024cvprw-selfsupervised,
title = {{Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy}},
author = {Kazimi, Bashir and Ruzaeva, Karina and Sandfeld, Stefan},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {71-81},
doi = {10.1109/CVPRW63382.2024.00012},
url = {https://mlanthology.org/cvprw/2024/kazimi2024cvprw-selfsupervised/}
}