| Popis: |
Deep learning has been rapidly developed and obtained great achievements with a dataintensive condition. However, sufficient datasets are not always available in practical application. In the absence of data, humans can still perform well in studying and recognizing new items while it becomes a hard task for the computer to learn and generate from a small dataset. Thus, researchers are increasingly interested in few-shot learning. The purpose of few-shot learning is to allow computers to carry out unknown tasks with a few examples. Recently, effective few-shot models have frequently been designed using transfer learning approaches, with the metric method being an important branch in transfer learning. This article reviews the metric methodologies for few-short learning, analyzing the development of the metric based few-shot learning in the following three categories: traditional metric methods, relation network based metric methods and graph based metric methods. Then it compares the effectiveness of those models on a representative dataset and illustrates the feature of each category. Finally, it discusses the potential future research fields. |
