  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.
