N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning.

ABSTRACT: 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 d

SUBMITTER: Li Y 

PROVIDER: S-EPMC9718842 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature