Research on image classification methods based on small sample learning

Small sample learning aims to solve the problem of scarcity of labeled data and improve the classification performance of the model under limited labeled data. This paper first introduces small sample learning, then introduces transfer learning, incremental learning and meta-learning learning methods and compares them, and puts forward the challenges faced by small sample learning and the future research direction. With the continuous progress of technology and in-depth research, the image classification method based on small sample learning is expected to provide more efficient and accurate solutions for practical applications.