| Autor: |
Jiyi Chen, Pengyu Li, Yutong Wang, Pei-Cheng Ku, Qing Qu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
APL Machine Learning, Vol 2, Iss 3, Pp 036106-036106-10 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2770-9019 |
| DOI: |
10.1063/5.0209339 |
| Popis: |
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 in an 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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