论文标题
神经符号生成艺术：初步研究
Neuro-Symbolic Generative Art: A Preliminary Study
论文作者
论文摘要
有两类的生成艺术方法：神经，其中一个深层模型经过训练以从数据分布中生成样本，以及符号或算法，其中艺术家设计了主要参数，并且自主系统在这些约束中生成样本。在这项工作中，我们提出了一种新的杂种类型：神经符号生成艺术。作为一项初步研究，我们对符号方法的样品进行了生成深的神经网络。我们通过人类的研究证明，受试者使用神经符号方法发现最终的工件和创建过程，比符号方法分别更具创造力，分别为61％和82％的时间。
There are two classes of generative art approaches: neural, where a deep model is trained to generate samples from a data distribution, and symbolic or algorithmic, where an artist designs the primary parameters and an autonomous system generates samples within these constraints. In this work, we propose a new hybrid genre: neuro-symbolic generative art. As a preliminary study, we train a generative deep neural network on samples from the symbolic approach. We demonstrate through human studies that subjects find the final artifacts and the creation process using our neuro-symbolic approach to be more creative than the symbolic approach 61% and 82% of the time respectively.
