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问题上取得了成功，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），它能够有效控制域间合成样本的变化，并突出下游分类的注意力区域。具体而言，ParaGAN在循环投影中引入了投影距离参数，通过将源图像投影至决策边界以获取类别差异图。实验表明，在两个小规模医学数据集上，ParaGAN在可解释分类任务中始终优于现有增强方法。