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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.12354 |
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| _version_ | 1866929462375874560 |
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| author | Chen, Jiyi Li, Pengyu Wang, Yutong Ku, Pei-Cheng Qu, Qing |
| author_facet | Chen, Jiyi Li, Pengyu Wang, Yutong Ku, Pei-Cheng Qu, Qing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12354 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation Chen, Jiyi Li, Pengyu Wang, Yutong Ku, Pei-Cheng Qu, Qing Machine Learning Signal Processing 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. |
| title | Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2403.12354 |