## 1 Introduction  
In recent years, deep neural networks (DNNs) have achieved tremendous success in various machine learning tasks, such as face recognition [1], image classification [2], etc. [3; 4; 5]. DNNs require large amounts of labeled data for model training. However, due to the difficulty of classification and high cost of the labeling process, label noise is a common problem in datasets. Although DNNs are somewhat robust to label noise [6], they tend to overfit the data, thus making them prone to memorizing even completely random noise. In order to efficiently train DNNs, the existence of label noise must be considered, and the corresponding algorithms must be developed to alleviate their adverse effects in model training. In machine learning, noise contained in the training dataset can be divided into two categories: feature noise and label noise [7]. Feature noise corresponds to the corruption in observed data features, whereas label noise means the change in label from its true class. Both types of noise may cause a significant decrease in the performance of model training and classification. This study focused on the label noise, which is considered to have a greater impact on the performance of model training and classification than the feature noise. This is due to several factors: the label is unique for each data value, although features are multiple; and the importance of each feature varies, whereas the label always has a significant impact [8]. Label noise is unavoidable in application scenarios, and it is a natural consequence of the dataset collection process. Obtaining massive and clean annotators in application scenarios is expensive and time-consuming, especially for some professional datasets, in which expert knowledge is required and labels provided by different annotators may be inconsistent. Prior research has proven that noisy labels can significantly affect the performance of DNNs in image classification [9]. Several attempts have been made to solve the noisy data problem in the network training process through noisy label learning technologies and have been successfully applied to several application scenarios [10; 11; 12]. The techniques mainly include the methods that learn noisy labels directly and the semi-supervised methods which first train models on a small subset of clean labeled samples and then generalize the model trained on that subset to unlabeled or weakly labeled sample dataset. However, those methods may suffer from the problems of poor generalization ability, difficulties in identifying mislabeled samples from hard samples, and increased computational cost and training time. Curriculum learning [13] is a sample choosing algorithm which was inspired by human cognition and proposes to start from easy samples and utilize harder samples to guide training. Curriculum learning can accelerate the training of machine learning models and enable the model to obtain a better generalization performance. Most contemporary curriculum learning methods are based on the framework design of the difficulty measure and training scheduler. Among them, difficulty measurers determine the learning priority of each data value, and training schedulers determine when to input the hard data into training and how much to put in each time. Depending on whether the difficulty measurer and training scheduler can be designed automatically, curriculum learning can be divided into predefined curriculum learning and automatic curriculum learning. Image classification using DNNs requires the purification of the dataset as a preprocessing stage for model training: in this case, the concept of curriculum learning can be introduced to improve the model training performance. However, in curriculum learning, the specification of \"easy samples\" is achieved through the difficulty measurer, which is a task-specific issue. Moreover, when to input the hard data into training and how much to put in each time with the training scheduler is always hard to decide.