A Survey of Few-Shot Learning Research Based on Deep Neural Network
Pengjin Wu · Frontiers in Computing and Intelligent Systems · 2022
With the successful development of deep learning techniques in recent years, deep neural networks have achieved excellent results in both computer vision and natural language processing by relying on large-scale datasets but still face significant challenges in solving the problem of learning from few-shot. Inspired by the ability of humans to learn to recognize objects as a way to simulate the cognitive process of learning from a small sample size, few-shot learning is a hot topic of research in deep neural networks today. It is also a significant and challenging problem. This paper first introduces the research background and definition of few-shot learning, introduces the relevant models, and summarizes and analyzes the common approaches to the problem of few-shot learning based on deep neural networks at the present stage, which are divided into four types: data augmentation, model fine-tuning, metric learning and meta-learning. Finally, popular datasets for few-shot learning are described, the paper is concluded and future research directions are discussed.