Raman spectroscopy generates large volumes of data in routine measurements, but most of it lacks labels, which limits the use of conventional supervised learning. The new work introduces a self-supervised learning framework that aims to exploit this unlabeled data directly.
The framework is built around a masked autoencoder, a model that learns by masking parts of the input and reconstructing them. It is designed to handle multiple domains of Raman spectra, and it is described as physics-aware, meaning that physical knowledge is incorporated into the pretraining process.
Since this is a single arXiv abstract, there are no independent results or comparisons to report. The contribution is the framework itself, which could help researchers put the large volumes of unlabeled Raman data to use without requiring manual annotation.