PAF: Perturbation-Aware Filtering for Open-Set Semi-Supervised Learning

Open-set semi-supervised learning (OSSL) has achieved notable progress in exploiting unlabeled data, yet most existing methods overlook the distinct sensitivities of indistribution (ID) and out-of-distribution (OOD) samples to semantic-preserving perturbations, resulting in suboptimal model performance. To address this limitation, we propose Perturbation-Aware Filtering (PAF), which leverages the behavioral difference between ID and OOD samples under perturbations and extends it into a representation-level signal for reliable OOD filtering. Specifically, PAF identifies OOD samples by measuring the representation instability under semantic-preserving perturbations. We then integrate PAF into a carefully designed two-stage training framework, allowing the model to exploit abundant unlabeled data in the first stage and gradually adapt to the openset setting with limited labels in the second stage. Extensive experimental results on widely-used OSSL benchmarks demonstrate that our proposed PAF approach achieves superior performance compared to state-of-the-art (SOTA) OSSL methods. Our code is available at https:// github.com/njustkmg/CVPR26-PAF.