Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy
<|reference_start|>Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy: 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.<|reference_end|>
@article{kazimi2024self-supervised,
    title={Self-Supervised Learning with Generative Adversarial Networks for
  Electron Microscopy},
    author={Bashir Kazimi and Karina Ruzaeva and Stefan Sandfeld},
    journal={arXiv preprint arXiv:2402.18286},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18286},
    primaryClass={cs.CV cond-mat.mtrl-sci cs.AI cs.LG}
}