论文阅读笔记《TADAM: Task dependent adaptive metric for improved few-shot learning》
核心思想 本文在基于度量学习的小样本算法的基础上提出了几点改进方案：度量放缩（Metric Scaling），任务条件（Task Conditioning）以及辅助任务合作训练（Auxiliary task co-training）。首先，对于度量函数 d ( z , c k ) d(z,c_k) d(z,ck)增加一个放缩系数 α \alpha α，使得分类概率 p ( y = k ∣ x
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相关文章
- 1. TADAM: Task dependent adaptive metric for improved few-shot learning阅读笔记
- 2. TADAM:Task dependent adaptive metric for improved few-shot learning
- 3. 论文阅读笔记《Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning》
- 4. 论文阅读：Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning（ICCV 2019）
- 5. 论文阅读笔记《MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification》
- 6. 论文阅读Adaptive Subspaces for Few-Shot Learning
- 7. 论文阅读笔记《Task-Agnostic Meta-Learning for Few-shot Learning》
- 8. 论文阅读笔记《TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning》
- 9. 论文阅读笔记《Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning》
- 10. Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning 论文笔记