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This research presents a physics-aware self-supervised learning framework using masked autoencoders to process multi-domain Raman spectroscopy data, effectively addressing the challenge of limited labeled datasets in scientific imaging. By integrating differentiable spectral peak modeling into the training objective, the method improves representation transferability and robustness across diverse applications like forensic analysis and materials science. The study highlights that attention-based probing uncovers richer information than standard linear evaluation, offering a more accurate assessment of learned features in low-data regimes.
Physics-Aware Self-Supervised Pretraining on Multi-Domain Raman Spectra
arXiv:2610.23955v1 Announce Type: new Abstract: Raman spectroscopy generates large volumes of unlabeled data that are difficult to exploit with conventional supervised learning. This work introduces a multi-domain self-supervised learning framework based on a masked autoencoder (MAE) for learning transferable Raman spectral representations from heterogeneous biological, chemical, forensic, and materials datasets. The proposed pretraining strategy incorporates physics-aware objectives by combining masked intensity reconstruction with a sparse Voigt-profile decoder that models spectral peak structure through differentiable peak centers, widths, and amplitudes. A denoising objective is further included to improve robustness to spectral corruption. The learned encoder is evaluated on substance classification, component identification, concentration estimation, and spectral denoising under linear probing, attention-based probing, end-to-end fine-tuning, and training from scratch. Experimental results show that self-supervised pretraining is most beneficial for challenging tasks with limited labeled data. Attention-based probing also reveals substantially more useful information in the frozen representation than linear probing, indicating that conventional linear evaluation may underestimate representation quality. In addition, the benefits of pretraining become more pronounced with increasing encoder capacity. Overall, the proposed framework demonstrates that physics-aware, multi-domain self-supervised pretraining can improve label efficiency, robustness, and transferability across diverse Raman spectroscopy tasks.
Abstract:Raman spectroscopy generates large volumes of unlabeled data that are difficult to exploit with conventional supervised learning. This work introduces a multi-domain self-supervised learning framework based on a masked autoencoder (MAE) for learning transferable Raman spectral representations from heterogeneous biological, chemical, forensic, and materials datasets. The proposed pretraining strategy incorporates physics-aware objectives by combining masked intensity reconstruction with a sparse Voigt-profile decoder that models spectral peak structure through differentiable peak centers, widths, and amplitudes. A denoising objective is further included to improve robustness to spectral corruption. The learned encoder is evaluated on substance classification, component identification, concentration estimation, and spectral denoising under linear probing, attention-based probing, end-to-end fine-tuning, and training from scratch. Experimental results show that self-supervised pretraining is most beneficial for challenging tasks with limited labeled data. Attention-based probing also reveals substantially more useful information in the frozen representation than linear probing, indicating that conventional linear evaluation may underestimate representation quality. In addition, the benefits of pretraining become more pronounced with increasing encoder capacity. Overall, the proposed framework demonstrates that physics-aware, multi-domain self-supervised pretraining can improve label efficiency, robustness, and transferability across diverse Raman spectroscopy tasks.
Machine Learning (cs.LG); Image and Video Processing (eess.IV)
arXiv:2610.23955 [cs.LG]
(or arXiv:2610.23955v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.23955 Focus to learn more arXiv-issued DOI via DataCite
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