Finding Task-Relevant Features for Few-Shot Learning by Category Traversal

時間 2021-01-13

標籤 few shot learning 简体版

原文原文鏈接

動機當前的few-shot learning在學習過程中，並沒有關注support set中類與類之間的關係。作者認爲在一個support set中，如果能橫穿整個support set觀察所有特徵，這對於最後結果的調整是很有幫助的，兩個例子：對於上圖來說，這是一個分類任務。具體步驟還是和metric learning一樣，計算query 與support中每一個的距離，最後得到結果。（a）

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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. 論文閱讀筆記《Finding Task-Relevant Features for Few-Shot Learning by Category Traversal 》
4. Automatically Learning Semantic Features for Defect Prediction
5. Learning Region Features for Object Detection
6. Learning Deep Features for Discriminative Localization
7. Learning Hierarchical Features for Scene Labeling
8. Learning Deep Features for Discriminative Localization筆記
9. 論文閱讀-《Learning Deep Features for Discriminative Localization》
10. [解讀] Deep Clustering for Unsupervised Learning of Visual Features

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