@inproceedings{564fa277a8bf41fa93de654b53a79de8,
title = "Few-Shot Learning for Images Classification Considering Feature Variance",
abstract = "Few-shot learning aims to solve the problem of learning with limited samples. However, existing metric-based meta-learning models have the issue of insufficient attention to feature representation capability. Therefore, this paper introduces the feature variance loss during network training to learn more powerful features. Experimental results demonstrate that our method achieves good learning performance on the miniImageNet and tieredImageNet datasets.",
keywords = "Few-shot learning, feature variance, image classification, meta-learning, metric learning",
author = "Lei Wang and Minhao Xue and Li Wang and Ming Cheng",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 3rd International Conference on Electronic Information Engineering and Computer, EIECT 2023 ; Conference date: 17-11-2023 Through 19-11-2023",
year = "2023",
doi = "10.1109/EIECT60552.2023.10442062",
language = "英语",
series = "2023 3rd International Conference on Electronic Information Engineering and Computer, EIECT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "508--511",
booktitle = "2023 3rd International Conference on Electronic Information Engineering and Computer, EIECT 2023",
address = "美国",
}