JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning
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. Despite their strong empirical performance, these methods typically struggle with two critical bottlenecks: majority classes tend to dominate the learning process, which is amplified by incorrect pseudo-labels, leading to biased models. Furthermore, noisy early pseudo-labels prevent the model from ...
| Authors | Ali Aghababaei-Harandi, Aude Sportisse, Massih-Reza Amini |
|---|---|
| Primary category | cs.LG |
| Published | 22 April 2026 |
| Last updated at source | 26 August 2026 |
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