[論文筆記] Few-Shot Learning with Global Class Representations
[論文筆記] Few-Shot Learning with Global Class Representations 0. 寫在前面的話 解決的問題 由於基類和新類之間存在嚴重的樣本不均衡問題，導致容易過擬合到基類數據 訓練模型的時候，使用來自novel class的樣本。 新穎點 在few-shot裏面使用類原型。 有點聚類的思想 訓練數據包括來自novel class的樣本。 面臨的困難- b
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相關文章
- 1. 論文閱讀筆記《Few-Shot Learning with Global Class Representations》
- 2. Few-Shot Learning with Global Class Representations筆記整理
- 3. 論文筆記----DeepWalk: Online Learning of Social Representations
- 4. 論文筆記：DeepWalk: Online Learning of Social Representations
- 5. Machine Learning & Deep Learning 論文閱讀筆記
- 6. 論文閱讀筆記《Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy》
- 7. 論文筆記- Improving Word Representations via Global Context and Multiple Word Prototypes
- 8. 【論文筆記——DIM】Learning Deep Representations By Mutual Information Estimation and Maximization
- 9. 論文筆記——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 10. Multi-view Learning with Incomplete Views論文筆記