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- W3045332022 endingPage "107729" @default.
- W3045332022 startingPage "107729" @default.
- W3045332022 abstract "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." @default.
- W3045332022 created "2020-07-29" @default.
- W3045332022 creator A5010479652 @default.
- W3045332022 creator A5044307720 @default.
- W3045332022 creator A5062362866 @default.
- W3045332022 creator A5082261154 @default.
- W3045332022 date "2020-12-01" @default.
- W3045332022 modified "2023-10-13" @default.
- W3045332022 title "Deep learning methods for solving linear inverse problems: Research directions and paradigms" @default.
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