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We present a novel discriminator for GANs that improves realness anddiversity of generated samples by learning a structured hypersphere embeddingspace using spherical circles. The proposed discriminator learns to populaterealistic samples around the longest spherical circle, i.e., a great circle,while pushing unrealistic samples toward the poles perpendicular to the greatcircle. Since longer circles occupy larger area on the hypersphere, theyencourage more diversity in representation learning, and vice versa.Discriminating samples based on their corresponding spherical circles can thusnaturally induce diversity to generated samples. We also extend the proposedmethod for conditional settings with class labels by creating a hypersphere foreach category and performing class-wise discrimination and update. Inexperiments, we validate the effectiveness for both unconditional andconditional generation on standard benchmarks, achieving the state of the art.

CircleGAN: Generative Adversarial Learning across Spherical Circles