Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning 论文笔记
摘要 如何利用少量的数据学习到一个generalizable 的分类器目前仍旧是小样本学习的一个挑战，基于这个出发点，作者把元学习(meta-learning)和深度度量学习和归纳推理想结合，通过探索每个任务中成对约束和正则化，我们将适应过程明确地公式化为标准的半定规划问题。作者针对每个任务设计了一个情节性的(episodic-wise)度量矩阵来将通用的，任务不可知的编码空间转换到一个可判别性高
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相关文章
- 1. 论文阅读笔记《Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning》
- 2. 论文阅读：Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning（ICCV 2019）
- 3. 论文阅读笔记《TADAM: Task dependent adaptive metric for improved few-shot learning》
- 4. TADAM: Task dependent adaptive metric for improved few-shot learning阅读笔记
- 5. 【论文学习笔记】Transductive Unbiased Embedding for Zero-Shot Learning (2018_CVPR)
- 6. 【论文笔记】Deep Metric Learning via Facility Location
- 7. TADAM:Task dependent adaptive metric for improved few-shot learning
- 8. (Fewshot detection)Review:RepMet: Representative-based metric learning for few-shot detection
- 9. 论文阅读笔记《Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning》
- 10. 论文笔记《Deep Cosine Metric Learning for Person Re-Identification》