論文閱讀筆記《Finding Task-Relevant Features for Few-Shot Learning by Category Traversal 》
核心思想 本文在度量學習算法的基礎上提出了一種特徵學習模塊，用於改進原有算法特徵提取網絡的表徵能力，進而提高小樣本分類的準確性。本文設計的種類遍歷模塊（Category Traversal Module，CTM）可以作爲一種即插即用的模塊，直接添加到原有算法的網絡中。相對於原有的特徵提取網絡，CTM有針對性地提取了「類內共有特徵（intra-class commonality）」和「類間獨有特
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相關文章
- 1. Finding Task-Relevant Features for Few-Shot Learning by Category Traversal 論文筆記
- 2. Finding Task-Relevant Features for Few-Shot Learning by Category Traversal論文筆記
- 3. Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
- 4. 論文閱讀-《Learning Deep Features for Discriminative Localization》
- 5. 論文閱讀筆記：Learning Deep Features for Discriminative Localization
- 6. 《Learning Features and Parts for Fine-Grained Recognition》論文閱讀筆記
- 7. Machine Learning & Deep Learning 論文閱讀筆記
- 8. 論文閱讀筆記（四十）：Learning Spatiotemporal Features with 3D Convolutional Networks（C3D）
- 9. 論文筆記：Learning Region Features for Object Detection
- 10. Learning Deep Features for Discriminative Localization論文筆記