  As a promising area in artificial intelligence, a new learning paradigm,
called Small Sample Learning (SSL), has been attracting prominent research
attention in the recent years. In this paper, we aim to present a survey to
comprehensively introduce the current techniques proposed on this topic.
Specifically, current SSL techniques can be mainly divided into two categories.
The first category of SSL approaches can be called "concept learning", which
emphasizes learning new concepts from only few related observations. The
purpose is mainly to simulate human learning behaviors like recognition,
generation, imagination, synthesis and analysis. The second category is called
"experience learning", which usually co-exists with the large sample learning
manner of conventional machine learning. This category mainly focuses on
learning with insufficient samples, and can also be called small data learning
in some literatures. More extensive surveys on both categories of SSL
techniques are introduced and some neuroscience evidences are provided to
clarify the rationality of the entire SSL regime, and the relationship with
human learning process. Some discussions on the main challenges and possible
future research directions along this line are also presented.
