论文标题
解决线性反问题的深度学习方法：研究方向和范式
Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms
论文作者
论文摘要
线性反问题是各种科学领域的发展基础。已经进行了无数的尝试来解决不同应用中线性反问题的不同变体。如今，深度学习的快速发展为解决线性反面问题提供了一个新的观点，该问题具有各种精心设计的网络体系结构，从而在许多应用程序中促进了最先进的性能。在本文中，我们对深度学习的最新进展进行了全面调查，以解决各种线性反问题。我们回顾如何使用深度学习方法来解决不同的线性反问题，并探索结合传统方法中使用的知识的结构化神经网络体系结构。此外，我们确定了这一研究线的开放挑战和潜在的未来方向。
The linear inverse problem is fundamental to the development of various scientific areas. Innumerable attempts have been carried out to solve different variants of the linear inverse problem in different applications. Nowadays, the rapid development of deep learning provides a fresh perspective for solving the linear inverse problem, which has various well-designed network architectures results in state-of-the-art performance in many applications. In this paper, we present a comprehensive survey of the recent progress in the development of deep learning for solving various linear inverse problems. We review how deep learning methods are used in solving different linear inverse problems, and explore the structured neural network architectures that incorporate knowledge used in traditional methods. Furthermore, we identify open challenges and potential future directions along this research line.
