Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation

• URL: http://arxiv.org/abs/2403.12354v3
• Date: Sat, 17 Aug 2024 00:50:26 GMT
• Title: Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation
• Authors: Jiyi Chen, Pengyu Li, Yutong Wang, Pei-Cheng Ku, Qing Qu,
• Abstract summary: This work proposes a deep learning framework, namely Sim2Real, for spectral signal reconstruction in reconstructive spectroscopy. It focuses on the challenge of reconstructing real-world spectral signals under the extreme setting where only device-informed simulated data are available for training.
• Score: 23.24059547710097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work proposes a deep learning (DL)-based framework, namely Sim2Real, for spectral signal reconstruction in reconstructive spectroscopy, focusing on efficient data sampling and fast inference time. The work focuses on the challenge of reconstructing real-world spectral signals under the extreme setting where only device-informed simulated data are available for training. Such device-informed simulated data are much easier to collect than real-world data but exhibit large distribution shifts from their real-world counterparts. To leverage such simulated data effectively, a hierarchical data augmentation strategy is introduced to mitigate the adverse effects of this domain shift, and a corresponding neural network for the spectral signal reconstruction with our augmented data is designed. Experiments using a real dataset measured from our spectrometer device demonstrate that Sim2Real achieves significant speed-up during the inference while attaining on-par performance with the state-of-the-art optimization-based methods.

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