論文閱讀筆記《Large Margin 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（L-GNN，L-PN），其思想與上篇文章《Deep Triplet Ranking Networks for One-Shot Recognition》非常相似，就是提出一種三元損失函數，來引導網絡拉近類內距離，增加類間距離。算法的思想如下圖所示 圖(a)顯示了不帶有Large Margin損失函數
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相關文章
- 1. Machine Learning & Deep Learning 論文閱讀筆記
- 2. Inductive Representation Learning on Large Graphs 論文閱讀
- 3. 論文閱讀筆記《Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy》
- 4. Inductive Representation Learning on Large Graphs閱讀筆記
- 5. Inductive Representation Learning On Large Graphs【閱讀筆記】
- 6. 論文閱讀筆記《Active One-shot Learning》
- 7. Deep Mutual Learning論文閱讀筆記
- 8. 論文閱讀筆記《One-Shot Imitation Learning》
- 9. 文獻閱讀 - Distance Metric Learning for Large Margin Nearest Neighbor Classification
- 10. 論文筆記: Inductive Representation Learning on Large Graphs