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Towards Better Generalization of Deep Neural Networks via Non-Typicality Sampling Scheme | |
Peng, Xinyu1; Wang, Fei-Yue2; Li, Li3 | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
ISSN | 2162-237X |
2022-02-11 | |
页码 | 11 |
通讯作者 | Li, Li(li-li@tsinghua.edu.cn) |
摘要 | Improving the generalization performance of deep neural networks (DNNs) trained by minibatch stochastic gradient descent (SGD) has raised lots of concerns from deep learning practitioners. The standard simple random sampling (SRS) scheme used in minibatch SGD treats all training samples equally in gradient estimation. In this article, we study a new data selection method based on the intrinsic property of the training set to help DNNs have better generalization performance. Our theoretical analysis suggests that this new sampling scheme, called the nontypicality sampling scheme, boosts the generalization performance of DNNs through biasing the solution toward wider minima, under certain assumptions. We confirm our findings experimentally and show that more variants of minibatch SGD can also benefit from the new sampling scheme. Finally, we discuss an extension of the nontypicality sampling scheme that holds promise to enhance both generalization performance and convergence speed of minibatch SGD. |
关键词 | Training Estimation Deep learning Standards Optimization Noise measurement Convergence Deep learning generalization performance nontypicality sampling scheme stochastic gradient descent (SGD) |
DOI | 10.1109/TNNLS.2022.3147031 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2020AAA0108104] |
项目资助者 | National Key Research and Development Program of China |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000757938800001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47889 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Li, Li |
作者单位 | 1.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China 2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100080, Peoples R China 3.Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Dept Automat, Beijing 100084, Peoples R China |
推荐引用方式 GB/T 7714 | Peng, Xinyu,Wang, Fei-Yue,Li, Li. Towards Better Generalization of Deep Neural Networks via Non-Typicality Sampling Scheme[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:11. |
APA | Peng, Xinyu,Wang, Fei-Yue,&Li, Li.(2022).Towards Better Generalization of Deep Neural Networks via Non-Typicality Sampling Scheme.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,11. |
MLA | Peng, Xinyu,et al."Towards Better Generalization of Deep Neural Networks via Non-Typicality Sampling Scheme".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):11. |
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