Generative deep neural networks used in machine learning, like the Variational Auto-Encoders (VAE), and Generative Adversarial Networks (GANs) produce new objects each time when asked to do so with the constraint that the new objects remain similar to some list of examples given as input. However, this behavior is unlike that of human artists that change their style as time goes by and seldom return to the style of the initial creations. We investigate a situation where VAEs are used to sample from a probability measure described by some empirical dataset. 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 distribution.
翻译:在机器学习中使用的产生深层神经网络,如变异自动电离器和基因反转网络,每次被要求时都产生新对象,但新对象与作为投入的一些实例清单保持相似。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改变其风格,很少恢复初始创建的风格。我们调查了一种情况,即VAEs被利用从某些实验数据集描述的概率尺度中取样。根据最近关于Radon-Sobolev统计距离的工程,我们提出了一个数字范式,将与基因算法一起使用,满足以下两项要求:所创造的物体不重复,也不演化,以填充全部目标概率分布。