关键词: 小样本学习, 度量学习, 数据增强, 元学习, 深度神经网络

Abstract:

With the recent vigorous development of deep learning, Deep Neural Networks（DNN） have made exciting breakthrough in large-scale image classification and recognition tasks, but they still face huge challenges in solving few shot learning problems. Few Shot Learning（FSL） is defined as learning a model that can solve practical problems with a small number of supervised samples, which is of great significance in the field of deep learning. This prompts people to systematically combs the recent work of few shot learning of DNN, and divide the solution into four strategies according to the technology they used to solve the small sample learning problem：data augmentation, metric learning, external memory, parameter optimization. According to these strategies, it comprehensively reviews the existing few shot learning methods of DNN, and summarizes the performance of each strategy on relevant benchmarks. Finally, the limitations of the existing technology are emphasized and its future development direction is prospected to provide reference for future research work.

Key words: few shot learning, metric learning, data augmentation, meta learning, deep neural network