  Despite the potential benefits of data augmentation for mitigating the data
insufficiency, traditional augmentation methods primarily rely on the 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) which 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
potential to extend to multi-class classification.
