Beijing Information Science and Technology University
In recent years, with the development of machine learning, the classification system has made great progress. Its performance largely depends on the quality of the training samples. It is time-consuming to obtain high-quality labeled data. To reduce costs, there are lots of methods labeling training data, such as crowdsourcing and automated systems. However, these methods often cause that a large number of data is mislabeled, namely label noisy. Besides the previous difficulty, insufficient information, expert errors and coding errors are also influence labels. If label noise is not handled appropriately during the training process, it may reduce the prediction accuracy, or increase the complexity of the model. Therefore, the research of label noise is of great significance for promoting the application of deep learning in various fields and reducing the deployment cost of deep learning algorithms. In this paper, a comprehensive introduction is provided about this research topic. Specifically, the types and effect of label noise are introduced, and the processing methods of label noise are analyzed.
刁恩虎,佟强,李丹,等. 分类任务中标签噪声的研究综述[J]. 科学技术与工程, , ():复制