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.