Exploiting synthetic images for real-world image recognition

Abstract

Creating big datasets is often difficult or expensive which causes people to augment their dataset with rendered images. This often fails to significantly improve accuracy due to a difference in distribution between real and rendered datasets. This paper shows that the gap between synthetic and real-world image distributions can be closed by using GANs to convert the synthetic data to a dataset which has the same distribution as the real data. Training this GAN requires only a fraction of the dataset traditionally required to get a high classification accuracy. This converted data can subsequently be used to train a classifier with a higher accuracy than a classifier trained only on the real dataset.