In pseudo-labeling (PL), which is a type of semi-supervised learning, pseudo-labels are assigned based on the confidence scores provided by the classifier; therefore, accurate confidence is important for successful PL. In this study, we propose a PL algorithm based on an energy-based model (EBM), which is referred to as the energy-based PL (EBPL). In EBPL, a neural network-based classifier and an EBM are jointly trained by sharing their feature extraction parts. This approach enables the model to learn both the class decision boundary and input data distribution, enhancing confidence calibration during network training. The experimental results demonstrate that EBPL outperforms the existing PL method in semi-supervised image classification tasks, with superior confidence calibration error and recognition accuracy.

翻译：在伪标签学习（一种半监督学习方法）中，伪标签是根据分类器提供的置信度分数进行分配的；因此，准确的置信度对于成功的伪标签学习至关重要。本研究提出了一种基于能量模型的伪标签学习算法，称为基于能量模型的伪标签学习（EBPL）。在EBPL中，基于神经网络的分类器与能量模型通过共享特征提取部分进行联合训练。该方法使模型能够同时学习类决策边界与输入数据分布，从而在网络训练过程中增强置信校准效果。实验结果表明，在半监督图像分类任务中，EBPL在置信校准误差和识别精度方面均优于现有伪标签学习方法。