Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
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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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
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- 4. Automatically Learning Semantic Features for Defect Prediction
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- 7. Learning Hierarchical Features for Scene Labeling
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- 10. [解讀] Deep Clustering for Unsupervised Learning of Visual Features