JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning

ArXi:2604.21046v1 Announce Type: new Semi-supervised learning has emerged as a powerful paradigm for leveraging large amounts of unlabeled data to improve the performance of machine learning models when labeled data are scarce. Among existing approaches, methods derived from FixMatch have achieved state-of-the-art results in image classification by combining weak and strong data augmentations with confidence-based pseudo-labeling.