论文阅读笔记《Few-Shot Learning with Global Class Representations》
小样本学习&元学习经典论文整理||持续更新 核心思想 本文提出一种基于度量学习的小样本学习算法。与其他算法将训练集分成基础类别和新类别，进行两个阶段的训练方式不同，本文将包含大量样本的基础类别和包含少量样本的新类别合在一起进行训练，得到每个类别的表征（原型）称之为全局类别表征（Global Class Representations，GCR）。然后对于每个Episode中的支持集样本得到对应的
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相关文章
- 1. [论文笔记] Few-Shot Learning with Global Class Representations
- 2. Machine Learning & Deep Learning 论文阅读笔记
- 3. Few-Shot Learning with Global Class Representations笔记整理
- 4. 论文阅读笔记《Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy》
- 5. 论文阅读：DeepWalk Online Learning of Social Representations
- 6. 1604. Learning deep feature representations with domain guided dropout for re-id论文阅读笔记
- 7. 论文阅读笔记《Learning Compositional Representations for Few-Shot Recognition》
- 8. Deep Mutual Learning论文阅读笔记
- 9. 论文阅读笔记：FULLY CONVOLUTIONAL MULTI-CLASS MULTIPLE INSTANCE LEARNING
- 10. 论文阅读笔记《CANet: Class-Agnostic Segmentation Networks withAttentive Few-Shot Learning》