ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 互补学习：一种面向图像应用和噪声标注的深度神经网络训练方法

1. (北京航空航天大学中德联合软件研究所 北京 100191) (zjoe546@foxmail.com)
• 出版日期: 2017-12-01
• 基金资助:
国家重点研发计划项目(2016YFB0200100);国家自然科学基金项目(61732002)

### Training Deep Neural Networks for Image Applications with Noisy Labels by Complementary Learning

Zhou Yucong, Liu Yi, Wang Rui

1. (Sino-German Joint Software Institute, Beihang University, Beijing 100191)
• Online: 2017-12-01

Abstract: In recent years, deep neural networks (DNNs) have made great progress in many fields such as image recognition, speech recognition and natural language processing, etc. The rapid development of the Internet and mobile devices promotes the popularity of image applications and provides a large amount of data to be used for training DNNs. Also, the manually annotated data is the key of training DNNs. However, with the rapid growth of data scale, the cost of manual annotation is getting higher and the quality is hard to be guaranteed, which will damage the performance of DNNs. Combining the idea of easy example mining and transfer learning, we propose a method called complementary learning to train DNNs on large-scale noisy labels for image applications. With a small number of clean labels and a large number of noisy labels, we jointly train two DNNs with complementary strategies and meanwhile transfer the knowledge from the auxiliary model to the main model. Through experiments we show that this method can efficiently train DNNs on noisy labels. Compared with current approaches, this method can handle more complicated noise labels, which demonstrates its value for image applications.