基于生成对抗网络的自监督学习用于电子显微镜
Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy
AI总结：
本文探索了生成对抗网络在电子显微镜数据上的自监督预训练，证明其能提升多种下游任务性能，低复杂度微调模型优于高复杂度随机初始化模型，尤其在标注数据有限时优势显著。
摘要：
在这项工作中，我们探索了将生成对抗网络（GANs）用于自监督学习在电子显微镜数据集上的潜力。我们展示了自监督预训练如何促进一系列下游任务的高效微调，包括语义分割、去噪、噪声与背景去除以及超分辨率。通过改变模型复杂度和感受野大小的实验，我们观察到显著现象：复杂度较低的微调模型始终优于采用随机权重初始化的更复杂模型。我们证明了自监督预训练在电子显微镜背景下各种下游任务中的通用性，能够实现更快的收敛和更好的性能。我们得出结论，自监督预训练是一种强大的催化剂，在标注数据有限且计算成本的高效扩展至关重要的情况下尤为有利。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
