中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
EventMix: An efficient data augmentation strategy for event-based learning

文献类型：期刊论文

作者Shen, Guobin2,4; Zhao, Dongcheng4; Zeng, Yi1,2,3,4
刊名INFORMATION SCIENCES
出版日期2023-10-01
卷号644页码:11
ISSN号0020-0255
关键词Event based data augmentation Neuromorphic data Spiking neural networks Reasonable label assignment Gaussian mixture model
DOI10.1016/j.ins.2023.119170
通讯作者Zeng, Yi(yi.zeng@ia.ac.cn)
英文摘要High-quality and challenging event stream datasets play an important role in the design of an efficient event-driven mechanism that mimics the brain. Although event cameras can provide high dynamic range and low-energy event stream data, the scale is smaller and more difficult to obtain than traditional frame-based data, which restricts the development of neuromorphic computing. Data augmentation can improve the quantity and quality of the original data by processing more representations from the original data. This paper proposes an efficient data augmentation strategy for event stream data: EventMix. We carefully design the mixing of different event streams by Gaussian Mixture Model (GMM) to generate random 3D masks and achieve arbitrary shape mixing of event streams in the spatio-temporal dimension. By computing the relative distances of event streams, we propose a more reasonable way to assign labels to the mixed samples. The experimental results on multiple neuromorphic datasets have shown that our strategy can improve performance on neuromorphic classification tasks as well as neuromorphic human action recognition tasks both for ANNs and SNNs, and we have achieved state-of-the-art performance on DVS-CIFAR10, N-Caltech101, and DVS-Gesture datasets.
WOS关键词SPIKING ; DEEPER
资助项目National Key Research and Development Program[2020AAA0104305] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32070100]
WOS研究方向Computer Science
语种英语
出版者ELSEVIER SCIENCE INC
WOS记录号WOS:001026696600001
资助机构National Key Research and Development Program ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/53684]  
专题多模态人工智能系统全国重点实验室
通讯作者Zeng, Yi
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China
4.Chinese Acad Sci, Inst Automat, Brain Inspired Cognit Intelligence Lab, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Shen, Guobin,Zhao, Dongcheng,Zeng, Yi. EventMix: An efficient data augmentation strategy for event-based learning[J]. INFORMATION SCIENCES,2023,644:11.
APA Shen, Guobin,Zhao, Dongcheng,&Zeng, Yi.(2023).EventMix: An efficient data augmentation strategy for event-based learning.INFORMATION SCIENCES,644,11.
MLA Shen, Guobin,et al."EventMix: An efficient data augmentation strategy for event-based learning".INFORMATION SCIENCES 644(2023):11.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。