  Partial-label learning (PLL) is an important weakly supervised learning
problem, which allows each training example to have a candidate label set
instead of a single ground-truth label. Identification-based methods have been
widely explored to tackle label ambiguity issues in PLL, which regard the true
label as a latent variable to be identified. However, identifying the true
labels accurately and completely remains challenging, causing noise in pseudo
labels during model training. In this paper, we propose a new method called
CroSel, which leverages historical predictions from the model to identify true
labels for most training examples. First, we introduce a cross selection
strategy, which enables two deep models to select true labels of partially
labeled data for each other. Besides, we propose a novel consistency
regularization term called co-mix to avoid sample waste and tiny noise caused
by false selection. In this way, CroSel can pick out the true labels of most
examples with high precision. Extensive experiments demonstrate the superiority
of CroSel, which consistently outperforms previous state-of-the-art methods on
benchmark datasets. Additionally, our method achieves over 90\% accuracy and
quantity for selecting true labels on CIFAR-type datasets under various
settings.
