Training-set-free two-stage deep learning for Spectroscopic data de-noising

arXiv:2402.18830v1 Announce Type: cross
Abstract: De-noising is a prominent step in the spectra post-processing procedure. Previous machine learning-based methods are fast but mostly based on supervised learning and require a training set that may be typically expensive in real experimental measurements. Unsupervised learning-based algorithms are slow and require many iterations to achieve convergence. Here, we bridge this gap by proposing a training-set-free two-stage deep learning method. We show that the fuzzy fixed input in previous methods can be improved by …

abstract algorithms arxiv cond-mat.mtrl-sci cs.lg data deep learning experimental free machine machine learning physics.data-an post-processing processing set stage supervised learning training type unsupervised unsupervised learning