文章基本信息

标题：Improved Loss Function for Image Classification
本地全文：下载
作者：Chenrui Wen ; Xinhao Yang ; Ke Zhang 等
期刊名称：Computational Intelligence and Neuroscience
印刷版ISSN：1687-5265
电子版ISSN：1687-5273
出版年度：2021
卷号：2021
页码：1-8
DOI：10.1155/2021/6660961
出版社：Hindawi Publishing Corporation
摘要：An improved loss function free of sampling procedures is proposed to improve the ill-performed classification by sample shortage. Adjustable parameters are used to expand the loss scope, minimize the weight of easily classified samples, and further substitute the sampling function, which are added to the cross-entropy loss and the SoftMax loss. Experiment results indicate that improvements in all classification performance of our loss function are shown in various network architectures and on different datasets. To summarize, compared with traditional loss functions, our improved version not only elevates classification performance but also lowers the difficulty of network training.