Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets. In this paper, we propose a parameterized GAN (ParaGAN) that effectively controls the changes of synthetic samples among domains and highlights the attention regions for downstream classification. Specifically, ParaGAN incorporates projection distance parameters in cyclic projection and projects the source images to the decision boundary to obtain the class-difference maps. Our experiments show that ParaGAN can consistently outperform the existing augmentation methods with explainable classification on two small-scale medical datasets.

翻译：尽管当前的数据增强方法在缓解数据不足方面取得了成功，但传统增强主要局限于域内操作，而先进的生成对抗网络（GANs）生成的图像仍存在不确定性，尤其是在小规模数据集中。本文提出了一种参数化生成对抗网络（ParaGAN），它能有效控制合成样本在不同域间的变化，并突出关键注意力区域以辅助下游分类任务。具体而言，ParaGAN在循环投影中引入投影距离参数，将源图像投影至决策边界以获得类别差异图。实验表明，ParaGAN在两个小规模医学数据集上始终优于现有增强方法，并实现了可解释的分类结果。