论文标题
Circlegan：跨球圈的生成对抗学习
CircleGAN: Generative Adversarial Learning across Spherical Circles
论文作者
论文摘要
我们提出了一种新的gan歧视因子，该歧视因素通过使用球形圆来学习结构化的超晶体嵌入空间来改善产生样品的真实性和多样性。提出的歧视者学会了在最长的球形圆（即大圆圈）周围填充逼真的样品，同时将不切实际的样品推向垂直于大圆圈的极点。由于较长的圈子占据了超大球的较大面积，因此它们鼓励代表学习的更多多样性，反之亦然。因此，基于它们相应的球形圆而区分样品可以自然诱导生成的样品的多样性。我们还通过为每个类别创建超级球并执行班级歧视和更新，扩展了具有类标签的条件设置的建议方法。在实验中，我们验证了在标准基准上无条件和条件产生的有效性，从而达到了最新状态。
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.
