论文标题
变分神经细胞自动机
Variational Neural Cellular Automata
论文作者
论文摘要
在自然界中，细胞生长和分化的过程导致了生物的惊人多样性 - 藻类，海星，巨型红杉，tardgrades和orcas都是由相同的生成过程产生的。受到这种生物生成过程令人难以置信的多样性的启发，我们提出了一种生成模型，即变异神经细胞自动机（VNCA），该模型受到细胞生长和分化的生物学过程的启发。与以前的相关作品不同，VNCA是一种适当的概率生成模型，我们根据最佳实践对其进行评估。我们发现VNCA学会了很好地重建样品，尽管其参数相对较少，而简单的仅本地通信，但VNCA可以学会从以通用矢量格式编码的信息中生成大量输出。尽管就生成建模性能而言，当前最新的差距存在很大的差距，但我们表明VNCA可以学习纯粹的自组织数据的生成过程。此外，我们表明VNCA可以学习可以从重大损害中恢复的稳定吸引子的分布。
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.
