论文标题
解开元学习：了解几个射击任务的特征表示形式
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
论文作者
论文摘要
元学习算法会产生特征提取器，可在几乎没有分类的情况下实现最先进的性能。虽然文献富含元学习方法，但对于为什么生成的特征提取器表现良好，知之甚少。我们对元学习的潜在力学以及使用元学习训练的模型和经过经典训练的模型进行了更好的了解。在此过程中，我们介绍并验证了几个假设，以了解为什么元学习模型表现更好。此外，我们开发了一个常规器，可提高标准训练程序的性能，以进行几次射击分类。在许多情况下，我们的日常工作优于元学习，同时运行的数量级更快。
Meta-learning algorithms produce feature extractors which achieve state-of-the-art performance on few-shot classification. While the literature is rich with meta-learning methods, little is known about why the resulting feature extractors perform so well. We develop a better understanding of the underlying mechanics of meta-learning and the difference between models trained using meta-learning and models which are trained classically. In doing so, we introduce and verify several hypotheses for why meta-learned models perform better. Furthermore, we develop a regularizer which boosts the performance of standard training routines for few-shot classification. In many cases, our routine outperforms meta-learning while simultaneously running an order of magnitude faster.
