Despite　the　potential　benefits　of　data　augmentation　for　mitigating　data　insufficiency,　traditional　augmentation　methods　primarily　rely　on　prior　intra-domain　knowledge.　On　the　other　hand,　advanced　generative　adversarial　networks　(GANs)　generate　inter-domain　samples　with　limited　variety.　These　previous　methods　make　limited　contributions　to　describing　the　decision　boundaries　for　binary　classification.　In　this　paper,　we　propose　a　distance-guided　GAN　(DisGAN)　that　controls　the　variation　degrees　of　generated　samples　in　the　hyperplane　space.　Specifically,　we　instantiate　the　idea　of　DisGAN　by　combining　two　ways.　The　first　way　is　vertical　distance　GAN　(VerDisGAN)　where　the　inter-domain　generation　is　conditioned　on　the　vertical　distances.　The　second　way　is　horizontal　distance　GAN　(HorDisGAN)　where　the　intra-domain　generation　is　conditioned　on　the　horizontal　distances.　Furthermore,　VerDisGAN　can　produce　the　class-specific　regions　by　mapping　the　source　images　to　the　hyperplane.　Experimental　results　show　that　DisGAN　consistently　outperforms　the　GAN-based　augmentation　methods　with　explainable　binary　classification.　The　proposed　method　can　apply　to　different　classification　architectures　and　has　the　potential　to　extend　to　multi-class　classification.　We　provide　the　code　in　https://github.com/yXiangXiong/DisGAN.