论文标题
与艺术家的互动神经风格转移
Interactive Neural Style Transfer with Artists
论文作者
论文摘要
我们提出了互动绘画过程，其中画家和各种神经风格转移算法在真实的画布上相互作用。然后，了解这些算法的输出实现的目标至关重要，以描述我们的交互式实验中的创意代理。我们收集了一组成对的绘画图像图像，并根据神经风格转移算法的预测性提出了一种新的评估方法。我们指出了一些算法的不稳定性，并表明它们可以用来扩大由众多现有的神经风格转移算法综合的图像的多样性和令人愉悦的奇怪性。这些图像的多样性被认为是人类画家的灵感来源，将机器描绘成计算催化剂。
We present interactive painting processes in which a painter and various neural style transfer algorithms interact on a real canvas. Understanding what these algorithms' outputs achieve is then paramount to describe the creative agency in our interactive experiments. We gather a set of paired painting-pictures images and present a new evaluation methodology based on the predictivity of neural style transfer algorithms. We point some algorithms' instabilities and show that they can be used to enlarge the diversity and pleasing oddity of the images synthesized by the numerous existing neural style transfer algorithms. This diversity of images was perceived as a source of inspiration for human painters, portraying the machine as a computational catalyst.
