Generative IA networks, like the Variational Auto-Encoders (VAE), and Generative Adversarial Networks (GANs) produce new objects each time when asked to do so. However, this behavior is unlike that of human artists that change their style as times go by and seldom return to the initial point. We investigate a situation where VAEs are requested to sample from a probability measure described by some empirical set. Based on recent works on Radon-Sobolev statistical distances, we propose a numerical paradigm, to be used in conjunction with a generative algorithm, that satisfies the two following requirements: the objects created do not repeat and evolve to fill the entire target probability measure.

翻译：产生性IA网络,如变式自动计算器(VAE)和生成性反转网络(GANs),每次被要求这样做时都产生新的物体。然而,这种行为与人类艺术家不同,人类艺术家随着时间流逝而改变风格,很少回到初始点。我们调查要求VAE从某些经验集描述的概率度量中取样的情况。根据最近关于Radon-Sobolev统计距离的工程,我们提出一个数字范式,与基因算法一起使用,满足以下两个要求:所创造的物体不会重复和演化,以填补整个目标概率度量度。