TADAM:Task dependent adaptive metric for improved few-shot learning
本文基于度量学习，发现简单的度量尺度会完全改变少样本学习参数更新的本质，同时提出了一个简单且有效的任务依赖的度量空间学习方法，可以根据不同的任务进行特征提取。通过度量缩放的方式，作者将余弦相似度与欧拉距离在少样本学习上的差距缩小了10%，也就是说度量的选择没有那么重要。另外，还提出了辅助任务协同训练，使得具有任务依赖性的特征提取更容易训练，并且具有很好的泛化能力 背景 考虑episodic形式的K
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相关文章
- 1. TADAM: Task dependent adaptive metric for improved few-shot learning阅读笔记
- 2. 论文阅读笔记《TADAM: Task dependent adaptive metric for improved few-shot learning》
- 3. (Fewshot detection)Review:RepMet: Representative-based metric learning for few-shot detection
- 4. (Few-shot Detection)Review: fewshot adaptive faster r-cnn
- 5. 论文阅读：Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning（ICCV 2019）
- 6. 论文阅读笔记《Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning》
- 7. Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning 论文笔记
- 8. Similarity Metric Learning for Face Recognition2013
- 9. Ranked List Loss for Deep Metric Learning
- 10. 【Active Learning - 03】Adaptive Active Learning for Image Classification