Few-shot image data augmentation based on generative adversarial networks
Yang Pengkun，Li Jinlong，Hao Runlai
(School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China)
Abstract： In recent years, image data augmentation methods based on Generative Adversarial Networks (GANs) have shown great potential. However, generating highresolution, highfidelity images typically requires a large amount of training data, which contradicts the current lack of training data situation. To address this issue, a conditional GAN model that can stably train on fewshot, highresolution image datasets has been proposed for data augmentation. Experimental results on benchmark datasets indicate that this model, compared to the current stateoftheart models, is capable of generating more realistic images and achieving the lowest Fréchet Inception Distance (FID) score. Furthermore, using this model for data augmentation in image classification tasks effectively mitigates overfitting issues in classifiers.
Key words : generative adversarial networks; data augmentation; image classification
