Union-net: lightweight deep neural network model suitable for small data sets

Abstract

Traditional deep learning models prefer large data sets, and in reality small data sets are easier to obtain. It is more practical to build models suitable for small data sets. Based on CNN, this paper proposes the concept of union convolution to build a deep learning model Union-net that is suitable for small data sets. The Union-net has small model size and superior performance. In this paper, the model is tested based on multiple commonly used data sets. The experimental results show that Union-net outperforms most models when dealing with small datasets, and Union-net outperforms other models when dealing with complex classification tasks or dealing with few-shot datasets. The codes for this paper have been uploaded to https://github.com/yeaso/union-net .