Data Augmentation Based on Wasserstein Generative Adversarial Nets Under Few Samples

Abstract

Aiming at the problem of low accuracy of image classification under the condition of few samples, an improved method based on Wasserstein Generative Adversarial Nets is proposed. The small data sets are augmented by generating target samples through minimax game between the generator and the discriminator. Firstly, label information is introduced into the network to save the cost of manual labeling for the generated images, and then classifier loss is introduced to further improve the quality of generated images, and the classification accuracy is improved after expanding the data set.