論文閱讀筆記《Power Normalizing Second-order Similarity Network for Few-shot Learning》
核心思想 本文提出一種基於二階統計量進行相似性度量的小樣本學習算法。二階統計量如：方差、協方差、二階矩、自相關函數、功率譜、互相關函數、互功率譜等在圖像細化和場景理解等視覺領域有着較爲廣泛的應用，但採用二階統計量進行特徵表達，需要配合適當的聚合或池化方法。本文首先將圖像的特徵信息轉化爲二階統計量，並且引入Power Normalization（PN）技術對其進行池化處理，最後採用距離度量的方式
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相關文章
- 1. 論文閱讀筆記《Learning to Compare: Relation Network for Few-Shot Learning》
- 2. 【論文閱讀】Learning Deconvolution Network for Semantic Segmentation
- 3. Machine Learning & Deep Learning 論文閱讀筆記
- 4. 論文閱讀筆記《Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning》
- 5. Representation Learning for Attributed Multiplex Heterogeneous Network 論文閱讀筆記
- 6. 論文《Learning both Weights and Connections for Efficient Neural Network》閱讀筆記
- 7. 論文閱讀筆記《Self-attention relation network for few-shot learning》
- 8. 論文閱讀筆記《Edge-Labeling Graph Neural Network for Few-shot Learning》
- 9. 論文閱讀筆記《DPGN: Distribution Propagation Graph Network for Few-shot Learning》
- 10. Network-in-Network論文閱讀筆記