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

arXiv:2403.12354v1 Announce Type: new
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 …

abstract arxiv challenge cs.lg data deep learning eess.sp framework inference sampling signal simulation spectroscopy type work world