Improving classification accuracy using data augmentation on small data sets

作者：

Highlights：

• State of the art techniques for data augmentation applied to small data sets obtaining good quality synthetic data.

• Prediction accuracy can be increased in the range of 1–3% by using data Augmentation.

• GAN is the preferred model for small sets, while VAE is better for larger ones.

摘要

•State of the art techniques for data augmentation applied to small data sets obtaining good quality synthetic data.•Prediction accuracy can be increased in the range of 1–3% by using data Augmentation.•GAN is the preferred model for small sets, while VAE is better for larger ones.

论文关键词：Deep Learning,Data augmentation,GAN,VAE,Unbalanced sets

论文评审过程：Received 17 October 2019, Revised 23 June 2020, Accepted 24 June 2020, Available online 15 July 2020, Version of Record 21 July 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113696