VOC-Certifire: Certifiably Robust One-Shot Spectroscopic Classification via Randomized Smoothing.

ABSTRACT: Spectroscopic methods are advantageous for gas detection with applications ranging from safety to operational efficiency. Despite the potential of laser-based sensors, real-world challenges, such as noise, interference and unseen conditions, hinder the accurate identification of species. The use of conventional machine learning (ML) models is constrained by extensive data requirements and their limited adaptability to new conditions. Although augmentation-based strategies have proven to improve the robustness of machine learning models, they still do not offer a complete defense. To address these challenges, this study focuses on three primary goals: first, to detect pressure-induced spectral broadening using simple yet effective augmentations; second, to bypass the need for extensive data

SUBMITTER: Sy M 

PROVIDER: S-EPMC11411668 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature