Research on small sample image classification based on auxiliary meta-learning

In the field of computer vision, image classification is a very important branch, it‘s aims to classify different images into different category labels. Traditional image processing methods improve generalization ability and model accuracy by learning large amounts of data, but in practical applications, acquiring large amounts of labeled data is often very difficult and costly. Therefore, small sample Learning and meta-learning have become effective methods to solve this problem. This paper discusses its application in small sample image classification from the perspective of auxiliary meta-learning.