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- W3123112060 abstract "Deep learning plays an important role in the field of artificial intelligence, but in most situation, deep learning requires a larger number of training samples to complete high-precision learning. Few-shot learning, which only uses a small dataset to learn different tasks, has been extensively researched. It can be summarized into three methods: metric learning, optimization-based and model-based. In this paper, we develop a novel method based on Matching Networks: focusing on the relation between samples. We have proposed a simple method based on the idea that we can aggregate intra-class samples and separate inter-class samples. Our experiments on the Omniglot dataset and miniImageNet dataset show that our method outperforms the baseline networks." @default.
- W3123112060 created "2021-02-01" @default.
- W3123112060 creator A5001383535 @default.
- W3123112060 creator A5065885664 @default.
- W3123112060 date "2020-11-06" @default.
- W3123112060 modified "2023-09-26" @default.
- W3123112060 title "A Matching Network Focusing on the Relation between Samples" @default.
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- W3123112060 doi "https://doi.org/10.1109/iciba50161.2020.9276759" @default.
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