N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning
文献类型：期刊论文
| 作者 | Li, Yang4,5 ; Dong, Yiting3,5; Zhao, Dongcheng5; Zeng, Yi1,2,3,4,5
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| 刊名 | SCIENTIFIC DATA
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| 出版日期 | 2022-12-02 |
| 卷号 | 9期号:1页码:9 |
| DOI | 10.1038/s41597-022-01851-z |
| 通讯作者 | Zeng, Yi(yi.zeng@ia.ac.cn) |
| 英文摘要 | Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain. However, the current artificial intelligence systems meet difficulties in achieving this ability. Similar challenges also exist for biologically plausible spiking neural networks (SNNs). Datasets for traditional few-shot learning domains provide few amounts of temporal information. And the absence of neuromorphic datasets has hindered the development of few-shot learning for SNNs. Here, to the best of our knowledge, we provide the first neuromorphic dataset for few-shot learning using SNNs: N-Omniglot, based on the Dynamic Vision Sensor. It contains 1,623 categories of handwritten characters, with only 20 samples per class. N-Omniglot eliminates the need for a neuromorphic dataset for SNNs with high spareness and tremendous temporal coherence. Additionally, the dataset provides a powerful challenge and a suitable benchmark for developing SNNs algorithms in the few-shot learning domain due to the chronological information of strokes. We also provide the improved nearest neighbor, convolutional network, SiameseNet, and meta-learning algorithm in the spiking version for verification. |
| 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 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:000915276700006 |
| 出版者 | NATURE PORTFOLIO |
| 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/173211/51359]  |
| 专题 | 类脑智能研究中心_类脑认知计算 |
| 通讯作者 | Zeng, Yi |
| 作者单位 | 1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China 2.Chinese Acad Sci, Inst Automation, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Automat, Brain inspired Cognit Intelligence Lab, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Yang,Dong, Yiting,Zhao, Dongcheng,et al. N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning[J]. SCIENTIFIC DATA,2022,9(1):9. |
| APA | Li, Yang,Dong, Yiting,Zhao, Dongcheng,&Zeng, Yi.(2022).N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning.SCIENTIFIC DATA,9(1),9. |
| MLA | Li, Yang,et al."N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning".SCIENTIFIC DATA 9.1(2022):9. |
入库方式： OAI收割
来源：自动化研究所
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