Helping AI Understand Physics. Trustworthy Approaches to Hyperspectral Imaging
Date:
Can trust be measured? Does a computer always know what to do? When dealing with experimental measurements of any kind, the ability to assess stability and trustworthiness of a machine learning model is key to create efficient analysis tools. In the case of hyperspectral data, knowledge of the physics underlying their generation is an inductive bias useful to assess the confidence of quantitative predictions. However, the reconstruction of a hyperspectral signal is often prevented by the presence of noise and strong spectral interference. Recent developments show that, under sensible assumptions, mathematical transformations of the data can ease the automatic extraction of information in complicated situations. In this talk, we review the state-of-the-art in applications of near-infrared and laser-induced breakdown spectroscopy, with a specific focus on trustworthy AI issues and semantic segmentation of hyperspectral images.