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

arXiv: https://arxiv.org/abs/2402.18830 · HTML: https://mlsanity.com/paper/2402.18830/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 20 of 20)

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- [Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks](https://mlsanity.com/paper/2203.07537/) (2022)
- [Removing grid structure in angle-resolved photoemission spectra via deep learning method](https://mlsanity.com/paper/2210.11200/) (2022)

## Abstract (arXiv)

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 introducing an adaptive prior. Combined with more advanced optimization techniques, our approach can achieve five times acceleration compared to previous work. Theoretically, we study the landscape of a corresponding non-convex linear problem, and our results indicates that this problem has benign geometry for first-order algorithms to converge.