Modern GANs achieve remarkable performance in terms of generating realistic
and diverse samples. This has led many to believe that ``GANs capture the
training data manifold''. In this work we show that this interpretation is
wrong. We empirically show that the manifold learned by modern GANs does not
fit the training distribution: specifically the manifold does not pass through
the training examples and passes closer to out-of-distribution images than to
in-distribution images. We also investigate the distribution over images
implied by the prior over the latent codes and study whether modern GANs learn
a density that approximates the training distribution. Surprisingly, we find
that the learned density is very far from the data distribution and that GANs
tend to assign higher density to out-of-distribution images. Finally, we
demonstrate that the set of images used to train modern GANs are often not part
of the typical set described by the GANs' distribution.

对现代生成对抗网络（GANs）的研究表明，其学习到的流形不符合训练分布，而且对超出分布的图片赋予更高密度，学到的潜在编码的先验与训练分布相去甚远，所用于训练 GANs 的图像集合通常不符合 GANs 的分布描述。