Present machine learning methods have reached a higher level than human intelligence in image recognition and other tasks. However, recent machine learning methods, especially deep learning methods, rely heavily on a large number of annotation data that human cognition often does not need. This weakness greatly limits the application of deep learning method in practical problem. To solve this problem, learning from a few shot examples attracts more and more community’s research interest. In order to better understand the few shot learning problem, this study extensively discusses several popular few shot learning methods, including data augmentation methods, transfer learning methods, and meta learning methods. After the processes and core ingredients of different algorithms are discussed, the advantages and disadvantages of existing methods could be clearly seen in solving few shot learning problems. At the end of this paper, the points to future research directions are highlighted in the field of few shot learning problem.