变分神经元胞自动机
Variational Neural Cellular Automata
摘要：
在自然界中，细胞生长和分化的过程导致了生物体的惊人多样性-藻类，海星，巨杉，缓步动物和逆戟鲸都是由相同的生殖过程产生的。受这种生物生成过程令人难以置信的多样性的启发，我们提出了一个生成模型，变分神经细胞自动机（VNCA），它是松散的细胞生长和分化的生物过程的启发。与以往的相关工作，VNCA是一个适当的概率生成模型，我们评估它根据最佳实践。我们发现，VNCA学习重建样本以及，尽管其相对较少的参数和简单的本地通信，VNCA可以学习生成各种各样的输出从一个共同的矢量格式编码的信息。虽然有一个显着的差距，目前的最先进的生成建模性能方面，我们表明，VNCA可以学习一个纯粹的自组织生成过程的数据。此外，我们表明，VNCA可以学习的稳定吸引子，可以从重大损害中恢复的分布。
英文摘要：
In nature, the process of cellular growth and differentiation has lead to an amazing diversity of organisms -- algae, starfish, giant sequoia, tardigrades, and orcas are all created by the same generative process. Inspired by the incredible diversity of this biological generative process, we propose a generative model, the Variational Neural Cellular Automata (VNCA), which is loosely inspired by the biological processes of cellular growth and differentiation. Unlike previous related works, the VNCA is a proper probabilistic generative model, and we evaluate it according to best practices. We find that the VNCA learns to reconstruct samples well and that despite its relatively few parameters and simple local-only communication, the VNCA can learn to generate a large variety of output from information encoded in a common vector format. While there is a significant gap to the current state-of-the-art in terms of generative modeling performance, we show that the VNCA can learn a purely self-organizing generative process of data. Additionally, we show that the VNCA can learn a distribution of stable attractors that can recover from significant damage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
