Mitigating Confidence Miscalibration in Open-World Semi-Supervised Learning

Open-world semi-supervised learning aims to use limited labeled data from known classes to classify unlabeled samples that contain both known and novel classes. Most existing methods over-rely on known classes and tend to generalize incorrectly in complex open-world settings. This leads to degraded pseudo-label quality and the model is often overconfident in wrong predictions. To address this, this paper proposes a **M**itigating **C**onfidence **M**iscalibration Method in **Open**-World Semi-Supervised Learning (**OpenMCM**). By leveraging discriminative knowledge from known classes to guide learning in the novel class space, our method effectively mitigates overconfident misclassifications and improves discrimination accuracy. Furthermore, we propose an adaptive threshold calibration strategy to independently determine optimal decision boundary for each class. By integrating a high-reliable pseudo-label fusion mechanism, we enhance recognition stability for novel classes. Experiments on three benchmark datasets ($i.e.$, CIFAR-10, CIFAR-100, and ImageNet-100) show that our framework achieves performance improvements over existing state-of-the-art methods.