TADAM: Task dependent adaptive metric for improved few-shot learning閱讀筆記
人類通過看少數樣例甚至是一個樣例後，就可以學會識別從未見過的新類別，當然我們希望機器也可以具備這樣的能力，所以近些年few-shot learning得到了廣泛的關注，它的目標可以概括爲構建一個可以從少量標記數據中進行歸納的模型。這裏我們舉個例子來描述小樣本算法中的一個經典任務。在5-way 5-shot任務中，我們有25張圖片做爲support set，其中包含5張貓類照片，5張狗類張片，5張人
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相關文章
- 1. 論文閱讀筆記《TADAM: Task dependent adaptive metric for improved few-shot learning》
- 2. TADAM:Task dependent adaptive metric for improved few-shot learning
- 3. 論文閱讀筆記《Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning》
- 4. 論文閱讀：Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning（ICCV 2019）
- 5. 論文閱讀筆記《Deep Meta-Learning: Learning to Learn in the Concept Space》
- 6. Matching Networks for one Shot Learning 閱讀筆記
- 7. 論文閱讀筆記《TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning》
- 8. 論文閱讀筆記《MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification》
- 9. 論文閱讀筆記《Task-Agnostic Meta-Learning for Few-shot Learning》
- 10. Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification閱讀筆記