论文阅读笔记《Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning》
小样本学习&元学习经典论文整理||持续更新 核心思想 本文提出一种基于度量学习的小样本学习算法（CovaMNet），其从二阶统计量（协方差）的角度出发，通过构建各个样本的特征向量之间的协方差矩阵实现类别表征与距离度量。该算法的实现过程如下图所示 如图所示，查询集和支持集样本分别经过CNN提取特征，得到对应的特征图 X i ∈ R h × w × d X_i\in \mathbb{R}^{h\t
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相关文章
- 1. 论文阅读笔记《Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace》
- 2. 论文阅读 Prototypical Networks for Few-shot Learning
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