  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.
