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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.
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