Optimization as a model for few-shot learning 论文笔记
前言 本文提出了一个基于LSTM的meta-learner模型来学习最优化算法，该算法将用于训练另一个learner分类器（神经网络分类器）。meta-learner捕获了任务中的short-term知识，和所有任务中共有的long-term知识，由meta-learner模型优化的另一个learner分类器在每个任务上都能快速收敛。 除此之外，meta-learner模型还学习了适用于其它lea
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相关文章
- 1. 元学习论文OPTIMIZATION AS A MODEL FOR FEW-SHOT LEARNING的简单理解
- 2. Optimization as a model for few-shot learning. (优化一个模型，用于少样本学习)---论文阅读笔记
- 3. 论文笔记：A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
- 4. 论文笔记 Benchmarking Model-Based Reinforcement Learning
- 5. 【论文笔记】NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING
- 6. 论文笔记_稀疏_聚类:A New Simplex Sparse Learning Model to Measure Data Similarity for Clustering
- 7. 论文笔记：Visual Question Answering as a Meta Learning Task
- 8. 【论文笔记】Deep Learning on Graphs: A Survey
- 9. 论文笔记：SinGAN: Learning a Generative Model from a Single Natural Image
- 10. [论文笔记]：SinGAN: Learning a Generative Model from a Single Natural Image