Cross-Modulation Networks For Few-Shot Learning
作者是在基于度量的学习的模型基础上，通过Cross-Modulation方法将每个抽象级别的support and query examples 整合，从而达到更好的预测表现。 方法 论文的核心方法是将Feature-wise Linear Modulation（FiLM）方法引入到度量学习中，关于FiLM的思想和具体实现可查阅原论文，这里就简单介绍下。 FiLM 将一种条件决定的面向特征的仿射变
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相关文章
- 1. Machine Learning for Communication Networks
- 2. (Fewshot detection)Review:RepMet: Representative-based metric learning for few-shot detection
- 3. Learning Convolutional Neural Networks for Graphs
- 4. metapath2vec: Scalable Representation Learning for Heterogeneous Networks
- 5. Matching Networks for One Shot Learning
- 6. Prototypical Networks for Few-shot Learning
- 7. Cross-stitch Networks for Multi-task Learning
- 8. A Critical Review of Recurrent Neural Networks for Sequence Learning
- 9. 论文阅读 Prototypical Networks for Few-shot Learning
- 10. neuron networks for machine learning coursera课程笔记