中圖分類號：TP391
文獻標識碼：A
DOI:10.19358/j.issn.2097-1788.2023.06.013
引用格式：楊鵬坤，李金龍，郝潤來.基于生成對抗網(wǎng)絡(luò)的小樣本圖像數(shù)據(jù)增強技術(shù)［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)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