Quality Evaluation Assurance Levels for Deep Neural Networks Software

被引:3
作者
Nakajima, Shin [1 ]
机构
[1] Natl Inst Informat, Tokyo, Japan
来源
2019 INTERNATIONAL CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI) | 2019年
关键词
D O I
10.1109/taai48200.2019.8959916
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Quality of machine learning software products or services is dependent on datasets used for training. However, defining quality of datasets is difficult, which might bring about risks in business situations. Independent, third-party testing laboratories would mitigate the risks. This paper proposes quality evaluation assurance levels, which is a basis of a third-party evaluation and certification framework. Moreover, quality of machine learning software is indeed viewed from three perspectives: prediction performance quality, training mechanism quality, and lifecycle support quality enabling continuous operations.
引用
收藏
页数:6
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