論文閱讀筆記《Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning》
核心思想 本文提出一種採用圖神經網絡GNN的小樣本學習算法，本質上還是一種學習參數更新策略的元學習算法，使得模型參數能夠根據在基礎數據集上得到的模型和少量的新樣本進行更新，在適應新任務的同時，且不會忘記舊任務。整個網絡結構分成特徵提取網絡 F ( ⋅ ∣ θ ) F(\cdot|\theta) F(⋅∣θ)和分類器網絡 C ( ⋅ ∣ w ) C(\cdot|w) C(⋅∣w)兩個部分，特徵提
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相關文章
- 1. 論文閱讀筆記《Low-Shot Learning with Imprinted Weights》
- 2. 論文《Learning both Weights and Connections for Efficient Neural Network》閱讀筆記
- 3. 論文閱讀筆記《Attentive Weights Generation for Few Shot Learning via Information Maximization》
- 4. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification 論文閱讀筆記
- 5. 【論文閱讀筆記】Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
- 6. 論文閱讀筆記《Dense Classification and Implanting for Few-Shot Learning》
- 7. Machine Learning & Deep Learning 論文閱讀筆記
- 8. Learning a Deep ConvNet for Multi-label Classification with Partial Labels 論文筆記
- 9. 《Bag of Tricks for Image Classification with Convolutional Neural Networks》論文閱讀筆記
- 10. Stacked Denoising Autoencoders (SDAE)