arXiv Machine Learning By Jiahe Chen, Qian Shao, Qiyuan Chen, Jiaying He, Jintai Chen, Jian Wu, Hongxia Xu

Geometric Gradient Rectification for Safe Open-Set Semi-Supervised Learning

Read the original on arXiv Machine Learning →

arXiv:2606. 26973v1 Announce Type: cross Abstract: Open-set semi-supervised learning aims to leverage unlabeled data that may contain out-of-distribution outliers while maintaining performance on in-distribution classes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Aug 27

JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning

JEPAMatch introduces a new semi‑supervised learning framework that replaces traditional output‑thresholding with explicit geometric shaping of latent representations. By combining the FlexMatch loss with a latent‑space regularization inspired by LeJEPA, the method encourages isotropic Gaussian structure in the embedding space, mitigating class imbalance and noisy pseudo‑labels. Experiments on CIFAR‑100, STL‑10, and Tiny‑ImageNet show consistent performance gains and faster convergence compared to existing FixMatch‑based baselines.

By Ali Aghababaei-Harandi, Aude Sportisse, Massih-Reza Amini
arXiv AI
Jul 7

A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning

arXiv:2607. 03600v1 Announce Type: cross Abstract: Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional shifts between the clean source and adversarially perturbed target domains.

By Sushant Dagaji Desale, Rahul Mishra, Ashutosh Kumar Sinha