Optimization as a model for few-shot learning 論文筆記
前言 本文提出了一個基於LSTM的meta-learner模型來學習最優化算法，該算法將用於訓練另一個learner分類器（神經網絡分類器）。meta-learner捕獲了任務中的short-term知識，和所有任務中共有的long-term知識，由meta-learner模型優化的另一個learner分類器在每個任務上都能快速收斂。 除此之外，meta-learner模型還學習了適用於其它lea
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相關文章
- 1. 元學習論文OPTIMIZATION AS A MODEL FOR FEW-SHOT LEARNING的簡單理解
- 2. Optimization as a model for few-shot learning. (優化一個模型，用於少樣本學習)---論文閱讀筆記
- 3. 論文筆記：A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
- 4. 論文筆記 Benchmarking Model-Based Reinforcement Learning
- 5. 【論文筆記】NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING
- 6. 論文筆記_稀疏_聚類:A New Simplex Sparse Learning Model to Measure Data Similarity for Clustering
- 7. 論文筆記：Visual Question Answering as a Meta Learning Task
- 8. 【論文筆記】Deep Learning on Graphs: A Survey
- 9. 論文筆記：SinGAN: Learning a Generative Model from a Single Natural Image
- 10. [論文筆記]：SinGAN: Learning a Generative Model from a Single Natural Image