論文閱讀筆記《Learning to Compare: Relation Network for Few-Shot Learning》
核心思想 本文是繼Siamese Network， Matching Network 和 Prototypical Network之後又一篇採用度量學習思路解決小樣本分類任務的文章。與先前的研究聚焦學習一整可遷移的嵌入式表示並採用預先定義好的固定的度量方法不同，本文進一步學習一種可遷移的深度度量方式能夠比較圖像之間的關係。整個網絡分成兩個階段，第一階段是一個嵌入式模塊（用於提取特徵信息），第二
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相關文章
- 1. Learning to Compare: Relation Network for Few-Shot Learning 論文筆記
- 2. 論文閱讀筆記《Self-attention relation network for few-shot learning》
- 3. Machine Learning & Deep Learning 論文閱讀筆記
- 4. 小樣本學習 | Learning to Compare: Relation Network for Few-Shot Learning
- 5. Learning to Compare: Relation Network 源碼調試
- 6. 論文閱讀筆記《Meta-SGD: Learning to Learn Quickly for Few-Shot Learning》
- 7. 論文閱讀筆記《Learning to propagate labels: Transductive propagation network for few-shot learning》
- 8. 【論文閱讀】Learning Deconvolution Network for Semantic Segmentation
- 9. 論文《Learning both Weights and Connections for Efficient Neural Network》閱讀筆記
- 10. 論文閱讀筆記（三十三）：Relation Network for Object Detection