Application of GAN for Reducing Data Imbalance under Limited Dataset

Abstract

The paper discusses architectural and training improvements of generative adversarial network (GAN) model for stable training. The advanced GAN architecture is proposed combining these improvements and it is applied for augmentation of a tire joint nonconformity dataset used for classification applications. The dataset used is highly unbalanced with higher number of conformity images. This unbalanced and limited dataset of nonconformity identification poses challenges in developing accurate nonconformity classification models. Therefore, a research is carried out in the presented work to augment the nonconformity dataset along with increasing the balance between different nonconformity classes. The quality of generated images is improved by incorporating recent developments in GANs. The present study shows that the proposed advanced GAN model is helpful in improving the performance classification model by augmentation under a limited unbalanced dataset. Generated results of advanced GAN are evaluated using Fréchet Inception Distance (FID) score, which shows large improvement over styleGAN architecture. Further experiments for dataset augmentation using generated images show 12% improvement in classification model accuracy over the original dataset. The potency of augmentation using GAN generated images is experimentally proved using principal component analysis plots.