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 for electron microscopy datasets. We show how self-supervised pretraining facilitates efficient fine-tuning for a spectrum of downstream tasks%2C including semantic segmentation%2C denoising%2C noise %26 background removal%2C 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%2C allowing faster convergence and better performance. We conclude that self-supervised pretraining serves as a powerful catalyst%2C being especially advantageous when limited annotated data are available and efficient scaling of computational cost is important.?quality=80&w=800)
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