PyCIL: a Python toolbox for class-incremental learning

被引：1

作者：

Da-Wei ZHOU ^{[1
]}

Fu-Yun WANG ^{[1
]}

Han-Jia YE ^{[1
]}

De-Chuan ZHAN ^{[1
]}

机构：

[1] State Key Laboratory for Novel Software Technology, Nanjing University

来源：

Science China(Information Sciences) | 2023年 / 66卷 / 09期

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论]; TP312.1 [];

学科分类号：

081104 ; 0812 ; 081202 ; 0835 ; 1405 ;

摘要：

With the rapid development of deep learning, current deep models can learn a fixed number of classes with high performance. However, in our ever-changing world, data often come from the open environment, which is with stream format or available temporarily due to privacy issues. As a result, the classification model should learn new classes incrementally instead of restarting the training process.

引用

页码：291 / 292

页数：2

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