Recently, deep learning approaches have become the main research frontier for biological image reconstruction problems thanks to their high performance, along with their ultra-fast reconstruction times. However, due to the difficulty of obtaining matched reference data for supervised learning, there has been increasing interest in unsupervised learning approaches that do not need paired reference data. In particular, self-supervised learning and generative models have been successfully used for various biological imaging applications. In this paper, we overview these approaches from a coherent perspective in the context of classical inverse problems, and discuss their applications to biological imaging.

翻译：最近,深层次的学习方法由于其高性能和超快的重建时间,已成为生物图像重建问题的主要研究前沿,然而,由于难以为监督的学习获取匹配的参考数据,人们越来越关注不需要对齐参考数据的未经监督的学习方法,特别是,在各种生物图像应用中成功地使用了自我监督的学习和基因化模型。在本文件中,我们从典型的反面问题的角度从连贯的角度审视这些方法,并讨论其对生物成像的应用。