Review of optimization methods for supervised deep learning

Q3 Computer Science

中国图象图形学报 Pub Date : 2023-01-01 DOI:10.11834/jig.211139

Lin Jiang, Yifeng Zheng, Chen Che, Li Guohe, Wenjie Zhang

引用次数: 0

Abstract

： Deep learning technique has been developing intensively in big data era. However ， its capability is still chal⁃ lenged for the design of network structure and parameter setting. Therefore ， it is essential to improve the performance of the model and optimize the complexity of the model. Machine learning can be segmented into five categories in terms of learn⁃ ing methods ： 1 ） supervised learning ， 2 ） unsupervised learning ， 3 ） semi - supervised learning ， 4 ） deep learning ， and 5 ） reinforcement learning. These machine learning techniques are required to be incorporated in. To improve its fitting and

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有监督深度学习的优化方法综述

深度学习技术在大数据时代得到了深入的发展。但在网络结构设计和参数设置方面，其性能仍然受到挑战。因此，提高模型的性能和优化模型的复杂度是至关重要的。机器学习可以根据学习方法分为五类:1)监督学习，2)无监督学习，3)半监督学习，4)深度学习，5)强化学习。这些机器学习技术需要被纳入。以提高其适用性和

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

中国图象图形学报 Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

1.20

自引率

0.00%

发文量

6776

期刊介绍： Journal of Image and Graphics (ISSN 1006-8961, CN 11-3758/TB, CODEN ZTTXFZ) is an authoritative academic journal supervised by the Chinese Academy of Sciences and co-sponsored by the Institute of Space and Astronautical Information Innovation of the Chinese Academy of Sciences (ISIAS), the Chinese Society of Image and Graphics (CSIG), and the Beijing Institute of Applied Physics and Computational Mathematics (BIAPM). The journal integrates high-tech theories, technical methods and industrialisation of applied research results in computer image graphics, and mainly publishes innovative and high-level scientific research papers on basic and applied research in image graphics science and its closely related fields. The form of papers includes reviews, technical reports, project progress, academic news, new technology reviews, new product introduction and industrialisation research. The content covers a wide range of fields such as image analysis and recognition, image understanding and computer vision, computer graphics, virtual reality and augmented reality, system simulation, animation, etc., and theme columns are opened according to the research hotspots and cutting-edge topics. Journal of Image and Graphics reaches a wide range of readers, including scientific and technical personnel, enterprise supervisors, and postgraduates and college students of colleges and universities engaged in the fields of national defence, military, aviation, aerospace, communications, electronics, automotive, agriculture, meteorology, environmental protection, remote sensing, mapping, oil field, construction, transportation, finance, telecommunications, education, medical care, film and television, and art. Journal of Image and Graphics is included in many important domestic and international scientific literature database systems, including EBSCO database in the United States, JST database in Japan, Scopus database in the Netherlands, China Science and Technology Thesis Statistics and Analysis (Annual Research Report), China Science Citation Database (CSCD), China Academic Journal Network Publishing Database (CAJD), and China Academic Journal Network Publishing Database (CAJD). China Science Citation Database (CSCD), China Academic Journals Network Publishing Database (CAJD), China Academic Journal Abstracts, Chinese Science Abstracts (Series A), China Electronic Science Abstracts, Chinese Core Journals Abstracts, Chinese Academic Journals on CD-ROM, and China Academic Journals Comprehensive Evaluation Database.