SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data

ArXi:2605.08519v1 Announce Type: new Learning from scarce labeled data with a larger pool of unlabeled samples, known as semi-supervised few-shot learning (SS-FSL), remains critical for applications involving tabular data in domains like medicine, finance, and science. The existing SS-FSL methods often rely on self-supervised learning (SSL) frameworks developed for vision or language, which assume the availability of a natural form of data augmentations.