摘要：In recent years, deep learning has made great breakthroughs in the field of the machine learning. The application of deep learning can be especially useful for coping with some complicated problems. However, one of the biggest drawbacks of the deep learning is that it requires a great amount of manual data annotation, this issue requires a lot of labor costs. Therefore, in order to alleviate the burden of deep learning, Palatucci et al. proposed zero-shot learning in 2009. Zero-shot learning is a special scenario of transfer learning. In the zero-shot learning progress, the samples of training and test classes do not intersect. It is necessary to complete the training by realizing the knowledge transfer between training class and test class, in order to successfully identify the label of the test class instance. The significance of zero-shot learning lies not only in the identification of difficult-to-annotate, but also in the fact that this method simulates the human cognitive process of objects that have never been seen before, so the study of zero-shot learning will contribute to the advancement of human cognitive science research. In view of the application value, theoretical significance and future development potential of zero-shot learning, this paper systematically reviews the research progress of zero-shot learning. First, the definition of zero-shot learning is summarized, then we introduce four typical zero-shot learning models, and the systematic problems in the zero-shot learning and the solution methods are introduced. We have categorized and elaborated on the multiple models of zero-shot learning. At the end, we pointed out the problems that need to be solved in the further study of zero-shot learning and possible future development directions.
Copyright © 2020 Acta Automatica Sinica. All rights reserved.

ISSN号：0254-4156

卷、期、页：v 46,n 1,p1-23

发表日期：2020-01-01

收录情况：EI（工程索引）

发表期刊名称：Zidonghua Xuebao/Acta Automatica Sinica

通讯作者：张鲁宁

第一作者：左信,刘建伟

论文类型：期刊论文

论文概要：张鲁宁,左信,刘建伟，Research and Development on Zero-Shot Learning，Zidonghua Xuebao/Acta Automatica Sinica，2020，v 46,n 1,p1-23

论文题目：Research and Development on Zero-Shot Learning