Most of partial label learning methods assume that all training samples have a set of candidate la-bels,but there are still a large number of unlabeled data in many real applications.How to construct a learning model by using both the information contained in partial and unlabeled samples is the crucial prob-lem of partial semi-supervised learning.Aiming at image classification problem with only a small number of labeled and partially labeled samples and a large number of unlabeled data,this paper uses the consis-tency regularization and pseudo-labeled methods to develop the learning model.For partial labeled and un-labeled samples,the pseudo-labels were generated by the corresponding output distributions of their weak augmentations,and those of partial labeled samples were constrained in the candidate label sets.A new loss function including three items was designed,which can simultaneously use the supervised,weak su-pervised as well as unsupervised information contained in the data.The pseudo-labeled samples with high-confidence were selected to calculate the cross-entropy loss of their two kinds of augmentations to improve the sample reliability involved in the training process.The experiment results in this paper showed that showed that the proposed method(CR-PSSL)had higher accuracy and stability than the existing state-of-the-art semi-supervised learning method(FlexMatch)and representative partial label learning methods,and the convergence speed was also significantly improved.
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