論文閱讀筆記《Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning》
小樣本學習&元學習經典論文整理||持續更新 核心思想 本文提出一種基於度量學習的小樣本學習算法（CovaMNet），其從二階統計量（協方差）的角度出發，通過構建各個樣本的特徵向量之間的協方差矩陣實現類別表徵與距離度量。該算法的實現過程如下圖所示 如圖所示，查詢集和支持集樣本分別經過CNN提取特徵，得到對應的特徵圖 X i ∈ R h × w × d X_i\in \mathbb{R}^{h\t
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相關文章
- 1. 論文閱讀筆記《Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace》
- 2. 論文閱讀 Prototypical Networks for Few-shot Learning
- 3. Matching Networks for one Shot Learning 閱讀筆記
- 4. 論文《Matching Networks for One Shot Learning》閱讀
- 5. 論文閱讀筆記《RepMet Representative-based metric learning for classification and few-shot object detection》
- 6. 論文閱讀——FPGA based Accelerators ofDeep Learning Networks for Learning andClassification:A Review
- 7. Machine Learning & Deep Learning 論文閱讀筆記
- 8. (Fewshot detection)Review:RepMet: Representative-based metric learning for few-shot detection
- 9. 論文閱讀筆記《Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning》
- 10. 論文閱讀筆記《TADAM: Task dependent adaptive metric for improved few-shot learning》