Virtual sample generation for small sample learning: A survey, recent developments and future prospects

Abstract

Virtual sample generation (VSG) technology aims to generate virtual samples based on real samples, in order to expand the size of the datasets and improve model performance. However, there is limited research summarizing VSG technology, which motivates this paper. In recent years, VSG technology has grown as a crucial tool for augmenting datasets and enhancing model performance, particularly in the fields like image recognition, medicine, and quality control where small datasets are common issues. This paper aims to provide an updated review of VSG technology, focusing on three key techniques which are important for small sample analysis studies, including sampling-based, information diffusion-based, and Generative Adversarial Networks (GANs)-based technology. In this review, we seek to identify the key trends in this field and to provide insights regarding the opportunities and challenges.