Meta-SGD: Learning to learn quickly for few shot learning---翻译理解
Abstract 对于孤立地从头开始学习每个任务的算法来说，少镜头学习是一个挑战。相反，元学习从许多相关的任务中学习，元学习者可以用更少的例子更准确、更快地学习新任务，而元学习者的选择是至关重要的。在这篇论文中，我们开发了一种类似于sgd的、易于训练的元学习器，它可以在监督学习和强化学习上一步初始化和适应任何可微分的学习者。与流行的元学习器LSTM相比，元sgd在概念上更简单，更容易实现，并且可以
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相关文章
- 1. 论文阅读笔记《Meta-SGD: Learning to Learn Quickly for Few-Shot Learning》
- 2. 翻译-Neural Snowball for Few-Shot Relation Learning
- 3. Zero-shot Learning / One-shot Learning / Few-shot Learning
- 4. few-shot learning, zero-shot learning, one-shot learning,any-shot learning, C-way K-shot，Meta-learn
- 5. Prototypical Networks for Few-shot Learning
- 6. Few-shot-learning
- 7. Zero-shot Learning / One-shot Learning / Few-shot Learning/传统 Learning
- 8. Zero-shot / One-shot / Few-shot Learning
- 9. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS翻译
- 10. Cross-Modulation Networks For Few-Shot Learning