  In this paper, we propose ComGAN(ComparativeGAN) which allows the generator
in GANs to refer to the semantics of comparative samples(e.g. real data) by
comparison. ComGAN generalizes relativistic GANs by using arbitrary
architecture and mostly outperforms relativistic GANs in simple
input-concatenation architecture. To train the discriminator in ComGAN, we also
propose equality regularization, which fits the discriminator to a neutral
label for equally real or fake samples. Equality regularization highly boosts
the performance of ComGAN including WGAN while being exceptionally simple
compared to existing regularizations. Finally, we generalize comparative
samples fixed to real data in relativistic GANs toward fake data and show that
such objectives are sound in both theory and practice. Our experiments
demonstrate superior performances of ComGAN and equality regularization,
achieving the best FIDs in 7 out of 8 cases of different losses and data
against ordinary GANs and relativistic GANs.
