2026
PAF: Perturbation-Aware Filtering for Open-Set Semi-Supervised Learning
CVPR 2026
Open-set semi-supervised learning (OSSL) has achieved notable progress in exploiting unlabeled data, yet most existing methods overlook the distinct sensitivities of in-distribution (ID) and out-of-distribution (OOD) samples to semantic-preserving perturbations, resulting in suboptimal model perform