论文标题
从几个示例中学习：几次学习方法的摘要
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning
论文作者
论文摘要
很少有学习的学习是指从一些培训样本中学习数据中的基本模式的问题。需要大量的数据样本，许多深度学习解决方案都遭受了数据饥饿和广泛的计算时间和资源的影响。此外，由于问题或隐私问题的性质，而且数据准备成本不仅是由于问题的性质，因此通常无法获得数据。数据收集，预处理和标签是艰苦的人类任务。因此，很少有可以大大减少构建机器学习应用程序的周转时间的学习，这是一种低成本解决方案。该调查论文包括最近提出的几片学习算法的代表清单。鉴于学习动力和特征，在元学习，转移学习和混合方法的角度讨论了几乎没有学习问题的方法（即，几乎没有研究的学习问题的不同变化）。
Few-Shot Learning refers to the problem of learning the underlying pattern in the data just from a few training samples. Requiring a large number of data samples, many deep learning solutions suffer from data hunger and extensively high computation time and resources. Furthermore, data is often not available due to not only the nature of the problem or privacy concerns but also the cost of data preparation. Data collection, preprocessing, and labeling are strenuous human tasks. Therefore, few-shot learning that could drastically reduce the turnaround time of building machine learning applications emerges as a low-cost solution. This survey paper comprises a representative list of recently proposed few-shot learning algorithms. Given the learning dynamics and characteristics, the approaches to few-shot learning problems are discussed in the perspectives of meta-learning, transfer learning, and hybrid approaches (i.e., different variations of the few-shot learning problem).
