@article{10.1063/5.0209339,
    author = {Chen, Jiyi and Li, Pengyu and Wang, Yutong and Ku, Pei-Cheng and Qu, Qing},
    title = "{Sim2Real in reconstructive spectroscopy: Deep learning with augmented device-informed data simulation}",
    journal = {APL Machine Learning},
    volume = {2},
    number = {3},
    pages = {036106},
    year = {2024},
    month = {08},
    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 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.}",
    issn = {2770-9019},
    doi = {10.1063/5.0209339},
    url = {https://doi.org/10.1063/5.0209339},
    eprint = {https://pubs.aip.org/aip/aml/article-pdf/doi/10.1063/5.0209339/20121785/036106\_1\_5.0209339.pdf},
}