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- W3031197078 abstract "The goal of this tutorial is to explain step-by-step how to implement physics-based learning for the rapid prototyping of a computational imaging system. We provide a basic overview of physics-based learning, the construction of a physics-based network, and its reduction to practice. Specifically, we advocate exploiting the auto-differentiation functionality twice, once to build a physics-based network and again to perform physics-based learning. Thus, the user need only implement the forward model process for their system, speeding up prototyping time. We provide an open-source Pytorch implementation of a physics-based network and training procedure for a generic sparse recovery problem" @default.
- W3031197078 created "2020-06-05" @default.
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- W3031197078 date "2020-05-27" @default.
- W3031197078 modified "2023-09-27" @default.
- W3031197078 title "How to do Physics-based Learning" @default.
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