Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation
<|reference_start|>Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation: 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.<|reference_end|>
@article{chen2024sim2real,
    title={Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented
  Device-Informed Data Simulation},
    author={Jiyi Chen, Pengyu Li, Yutong Wang, Pei-Cheng Ku, Qing Qu},
    journal={arXiv preprint arXiv:2403.12354},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12354},
    primaryClass={cs.LG eess.SP}
}