论文阅读笔记《Power Normalizing Second-order Similarity Network for Few-shot Learning》
核心思想 本文提出一种基于二阶统计量进行相似性度量的小样本学习算法。二阶统计量如：方差、协方差、二阶矩、自相关函数、功率谱、互相关函数、互功率谱等在图像细化和场景理解等视觉领域有着较为广泛的应用，但采用二阶统计量进行特征表达，需要配合适当的聚合或池化方法。本文首先将图像的特征信息转化为二阶统计量，并且引入Power Normalization（PN）技术对其进行池化处理，最后采用距离度量的方式
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相关文章
- 1. 论文阅读笔记《Learning to Compare: Relation Network for Few-Shot Learning》
- 2. 【论文阅读】Learning Deconvolution Network for Semantic Segmentation
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- 6. 论文《Learning both Weights and Connections for Efficient Neural Network》阅读笔记
- 7. 论文阅读笔记《Self-attention relation network for few-shot learning》
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- 9. 论文阅读笔记《DPGN: Distribution Propagation Graph Network for Few-shot Learning》
- 10. Network-in-Network论文阅读笔记