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May, 2023
实例化最大间隔用于实用的小样本识别
Instance-based Max-margin for Practical Few-shot Recognition
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Minghao Fu, Ke Zhu, Jianxin Wu
TL;DR
提出一种实用的少样本学习(pFSL)框架,基于无监督的预训练模型,通过IbM2实现少样本的识别任务,实验证明IbM2方法在各种少样本或多样本情况中都比基准方法有更好的表现。
Abstract
In order to mimic the human
few-shot learning
(FSL) ability better and to make FSL closer to real-world applications, this paper proposes a practical FSL (pFSL) setting. pFSL is based on
unsupervised pretrained models
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