論文閱讀筆記《Attentive Weights Generation for Few Shot Learning via Information Maximization》
核心思想 本文提出一種基於權重生成的小樣本學習算法（AWGIM），這種類型的方法之前在小樣本目標檢測算法中見到過，就是直接用一個生成器生成分類器的權重參數。本文與其他相關算法（LEO）的區別在於在生成分類器權重時，不僅考慮支持集圖像，而且考慮查詢集圖像。爲了實現這一目的，本文引入了互信息（Mutual Information，MI）和注意力機制。整個網絡的流程如下圖所示 首先，介紹一下什麼
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相關文章
- 1. 論文閱讀筆記-CVPR 2020: Attentive Weights Generation for Few Shot Learning via Information Maximization
- 2. 論文閱讀筆記《Learning Embedding Adaptation for Few-Shot Learning》
- 3. 論文閱讀 Prototypical Networks for Few-shot Learning
- 4. 論文閱讀筆記《Few-Shot Learning Through an Information Retrieval Lens》
- 5. 論文閱讀筆記《Few-shot Learning via Saliency-guided Hallucination of Samples》
- 6. 論文閱讀筆記《Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders》
- 7. 論文閱讀-Prototype Rectification for Few-Shot Learning
- 8. 論文閱讀Adaptive Subspaces for Few-Shot Learning
- 9. 論文閱讀筆記《Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning》
- 10. 論文閱讀筆記《Meta-Learning of Neural Architectures for Few-Shot Learning》