Cross-Modulation Networks For Few-Shot Learning
作者是在基於度量的學習的模型基礎上，通過Cross-Modulation方法將每個抽象級別的support and query examples 整合，從而達到更好的預測表現。 方法 論文的核心方法是將Feature-wise Linear Modulation（FiLM）方法引入到度量學習中，關於FiLM的思想和具體實現可查閱原論文，這裏就簡單介紹下。 FiLM 將一種條件決定的面向特徵的仿射變
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相關文章
- 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. 論文閱讀 Prototypical Networks for Few-shot Learning
- 10. neuron networks for machine learning coursera課程筆記