Generating images for imbalanced dataset problem

Imbalanced dataset problem may occur when the number of instances of a certain class is much lower than others, resulting in a drop in the classification result of minority class. We propose the method of generating images from 3D modeling by some softwares to get enough images of minority class and supplement the dataset to re-balance it. Several deep networks are trained on these datasets. The experiment results are evaluated by F-measure and show that when the images are generated by enough models, the classification performance can be obviously improved.