論文閱讀筆記《TADAM: Task dependent adaptive metric for improved few-shot learning》
核心思想 本文在基於度量學習的小樣本算法的基礎上提出了幾點改進方案：度量放縮（Metric Scaling），任務條件（Task Conditioning）以及輔助任務合作訓練（Auxiliary task co-training）。首先，對於度量函數 d ( z , c k ) d(z,c_k) d(z,ck)增加一個放縮係數 α \alpha α，使得分類概率 p ( y = k ∣ x
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相關文章
- 1. TADAM: Task dependent adaptive metric for improved few-shot learning閱讀筆記
- 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《MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification》
- 6. 論文閱讀Adaptive Subspaces for Few-Shot Learning
- 7. 論文閱讀筆記《Task-Agnostic Meta-Learning for Few-shot Learning》
- 8. 論文閱讀筆記《TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning》
- 9. 論文閱讀筆記《Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning》
- 10. Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning 論文筆記