論文閱讀筆記《Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images》
核心思想 本文提出一種利用變分自動編碼器（VAE）生成原型圖像（Prototypical Images），並利用最近鄰算法解決小樣本的圖標或標誌分類問題的算法。整個算法思想很簡單，首先作者指出實際中我們拍攝採集到的圖片通常都會有背景模糊，形狀或光照干擾等因素，用這些圖片做小樣本分類的話自然效果很差。於是作者提出了原型圖像的概念（Prototypical Images與原型網絡 Prototyp
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相關文章
- 1. Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images 論文筆記
- 2. 論文閱讀 Prototypical Networks for Few-shot Learning
- 3. 論文：Variational Graph Auto-Encoders閱讀筆記
- 4. 論文閱讀筆記 Prototypical Networks for Few-shot Learning
- 5. 論文閱讀筆記《Few-Shot Learning with Localization in Realistic Settings》
- 6. Machine Learning & Deep Learning 論文閱讀筆記
- 7. 論文閱讀筆記《Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders》
- 8. 論文閱讀筆記《Few-Shot Learning with Graph Neural Networks》
- 9. 論文閱讀筆記《Baby Steps Towards Few-Shot Learning with Multiple Semantics》
- 10. 論文筆記：Prototypical Networks for Few-shot Learning