  We present a novel discriminator for GANs that improves realness and
diversity of generated samples by learning a structured hypersphere embedding
space using spherical circles. The proposed discriminator learns to populate
realistic samples around the longest spherical circle, i.e., a great circle,
while pushing unrealistic samples toward the poles perpendicular to the great
circle. Since longer circles occupy larger area on the hypersphere, they
encourage more diversity in representation learning, and vice versa.
Discriminating samples based on their corresponding spherical circles can thus
naturally induce diversity to generated samples. We also extend the proposed
method for conditional settings with class labels by creating a hypersphere for
each category and performing class-wise discrimination and update. In
experiments, we validate the effectiveness for both unconditional and
conditional generation on standard benchmarks, achieving the state of the art.
