Few sample learning (FSL) is significant and challenging in the field of machine learning. The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence since humans can readily establish their cognition to novelty from just a single or a handful of examples whereas machine learning algorithms typically entail hundreds or thousands of supervised samples to guarantee generalization ability. Despite the long history dated back to the early 2000s and the widespread attention in recent years with booming deep learning technologies, little surveys or reviews for FSL are available until now. In this context, we extensively review 300+ papers of FSL spanning from the 2000s to 2019 and provide a timely and comprehensive survey for FSL. In this survey, we review the evolution history as well as the current progress on FSL, categorize FSL approaches into the generative model based and discriminative model based kinds in principle, and emphasize particularly on the meta learning based FSL approaches. We also summarize several recently emerging extensional topics of FSL and review the latest advances on these topics. Furthermore, we highlight the important FSL applications covering many research hotspots in computer vision, natural language processing, audio and speech, reinforcement learning and robotic, data analysis, etc. Finally, we conclude the survey with a discussion on promising trends in the hope of providing guidance and insights to follow-up researches.

翻译：少样本学习（FSL）是机器学习领域中一项重要且具有挑战性的任务。能够成功地从极少量样本中学习并泛化，是区分人工智能与人类智能的显著标志，因为人类可以轻松地通过单个或少量示例建立对新颖事物的认知，而机器学习算法通常需要数百或数千个有监督样本才能保证泛化能力。尽管少样本学习的研究历史可追溯至21世纪初，且近年来随着深度学习技术的蓬勃发展而受到广泛关注，但迄今为止，关于FSL的综述或评论文章却寥寥无几。在此背景下，我们广泛回顾了从2000年至2019年间的300余篇FSL相关论文，并提供了及时且全面的综述。本文中，我们回顾了FSL的演进历史及当前进展，将FSL方法原则上分为基于生成模型和基于判别模型两类，并特别强调了基于元学习的FSL方法。我们还总结了FSL中几个近期涌现的扩展性主题，并综述了这些主题的最新进展。此外，我们重点介绍了FSL的重要应用，涵盖计算机视觉、自然语言处理、音频与语音、强化学习与机器人技术、数据分析等多个研究热点。最后，我们以对富有前景的研究趋势的讨论作为综述的结尾，旨在为后续研究提供指导与启示。