Despite the extensive studies on Generative Adversarial Networks (GANs), how to reliably sample high-quality images from their latent spaces remains an under-explored topic. In this paper, we propose a novel GAN latent sampling method by exploring and exploiting the hubness priors of GAN latent distributions. Our key insight is that the high dimensionality of the GAN latent space will inevitably lead to the emergence of hub latents that usually have much larger sampling densities than other latents in the latent space. As a result, these hub latents are better trained and thus contribute more to the synthesis of high-quality images. Unlike the a posterior "cherry-picking", our method is highly efficient as it is an a priori method that identifies high-quality latents before the synthesis of images. Furthermore, we show that the well-known but purely empirical truncation trick is a naive approximation to the central clustering effect of hub latents, which not only uncovers the rationale of the truncation trick, but also indicates the superiority and fundamentality of our method. Extensive experimental results demonstrate the effectiveness of the proposed method.
翻译:尽管对Generation Aversarial Networks(GANs)进行了广泛的研究,但如何可靠地从潜在空间对高质量图像进行取样仍然是一个未得到充分探讨的专题。在本文中,我们提出一个新的GAN潜伏取样方法,通过探索和利用GAN潜伏分布中心点前期,探索和利用GAN潜伏分布中心点。我们的主要见解是,GAN潜伏空间的高度维度将不可避免地导致中心层层的出现,这些中心层的取样密度通常远大于潜伏地区的其他潜层。结果,这些中心层的训练较好,因而对高质量图像的合成贡献更大。与“采摘”的外表不同,我们的方法效率很高,因为它是一种先验的方法,在图像合成之前先辨出高质量的潜伏点。此外,我们表明,众所周知但纯粹经验性的逃逸变技巧是对中心层层中心群集效应的天性近似,不仅揭示了逃逸技巧的原理,而且表明了我们方法的优越性和根本性。