论文阅读笔记《Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning》
核心思想 本文采用基于度量学习的方式实现小样本学习任务，但与其他基于度量学习的方式不同，本文通过比较图像与类别之间的局部描述子（Local Descriptor），来寻找与输入图像最接近的类别。本文的灵感来自于朴素贝叶斯最近邻算法（Naive-Bayes Nearest-Neighbor，NBNN），根据这一算法作者有两点发现：一、如果将一幅图像的特征信息压缩到一个紧凑的图像级别的表征（换言之
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