Data Augmentation with Improved Generative Adversarial Networks

Abstract

Data augmentation is always a routinely trick in neural network training to improve generalization of a model. However, traditional transformation based methods are domain-specific, the transformations are required to be carefully designed. Recently, Generative Adversarial Networks (GAN) has been proposed to generate new samples which match the real data distribution. But directly using GAN generated samples in data augmentation faces the problems of label absence and uncertain data quality. In this paper, we propose an efficient and robust data augmentation method using GAN generated samples. This method proposes a modified GAN to generate more diverse samples and label them with a soft distribution labeling method. With an improved stochastic gradient descent, all the data are used to train the final classifier. The experiments are conducted on the widely used datasets: MNIST, SVHN and CIFAR-10. Our method empirically obtains promising results, even with few original data.