Few-shot Learning with Meta Metric Learners
一、介紹 現有的基於元學習、度量學習的小樣本學習方法在處理diverse domains和various classes上存在侷限。元學習訓練一個meta learner預測具有相同結構，但針對不同任務網絡的權重。度量學習針對不同任務學習一個不隨任務改變，適應所有任務的度量。當任務間差異較大時，度量學習將會失敗，學不到這樣的度量。作者提出了一個元度量學習的方法，利用度量學習的匹配網絡作爲base
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