  Spectral Normalization is one of the best methods for stabilizing the
training of Generative Adversarial Network. Spectral Normalization limits the
gradient of discriminator between the distribution between real data and fake
data. However, even with this normalization, GAN's training sometimes fails. In
this paper, we reveal that more severe restriction is sometimes needed
depending on the training dataset, then we propose a novel stabilizer which
offers an adaptive normalization method, called ABCAS. Our method decides
discriminator's Lipschitz constant adaptively, by checking the distance of
distributions of real and fake data. Our method improves the stability of the
training of Generative Adversarial Network and achieved better Fr\'echet
Inception Distance score of generated images. We also investigated suitable
spectral norm for three datasets. We show the result as an ablation study.
