中图分类号：TP391
文献标识码：A
DOI:10.19358/j.issn.2097-1788.2023.06.013
引用格式：杨鹏坤，李金龙，郝润来.基于生成对抗网络的小样本图像数据增强技术［J］.网络安全与数据治理，2023，42（6）：79-84，102.

Few-shot image data augmentation based on generative adversarial networks

Abstract： In recent years, image data augmentation methods based on Generative Adversarial Networks (GANs) have shown great potential. However, generating highresolution, highfidelity 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 fewshot, highresolution image datasets has been proposed for data augmentation. Experimental results on benchmark datasets indicate that this model, compared to the current stateoftheart 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