論文閱讀筆記《Baby Steps Towards Few-Shot Learning with Multiple Semantics》
核心思想 本文提出一種結合多種語義信息的小樣本學習算法。首先作者提到人類的幼兒在學習新的事物時，通常是利用多種語義信息綜合學習的，比如你給他看一隻狗，他不僅接收到視覺和簡單的語義標籤信息，你還會給他描述這隻狗有金色的毛髮，它還會汪汪叫。如此一來幼兒就可接收到多種語義信息，幫助他學習識別狗。正是出於這種思想，作者提出利用多種更爲豐富的語義信息來幫助模型實現小樣本學習，網絡的結構如下圖所示。
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相關文章
- 1. 論文：Baby Steps Towards Few-Shot Learning with Multiple Semantics
- 2. Machine Learning & Deep Learning 論文閱讀筆記
- 3. 論文閱讀筆記：FULLY CONVOLUTIONAL MULTI-CLASS MULTIPLE INSTANCE LEARNING
- 4. SRN: Towards Accurate Scene Text Recognition with Semantic Reasoning Networks ---論文閱讀筆記
- 5. Sentiment Classification towards Question-Answering with Hierarchical Matching Network 論文閱讀筆記
- 6. 【論文閱讀筆記】Deep Neural Network Compression with Single and Multiple Level Quantization
- 7. [論文閱讀]Learning Discriminative Features with Multiple Granularityfor Person Re-Identification
- 8. Deep Mutual Learning論文閱讀筆記
- 9. 論文閱讀筆記（四十）：Learning Spatiotemporal Features with 3D Convolutional Networks（C3D）
- 10. 論文閱讀筆記《Active One-shot Learning》