This paper explores visual indeterminacy as a description for artwork created with Generative Adversarial Networks (GANs). Visual indeterminacy describes images which appear to depict real scenes, but, on closer examination, defy coherent spatial interpretation. GAN models seem to be predisposed to producing indeterminate images, and indeterminacy is a key feature of much modern representational art, as well as most GAN art. It is hypothesized that indeterminacy is a consequence of a powerful-but-imperfect image synthesis model that must combine general classes of objects, scenes, and textures.

中文翻译：

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GAN 艺术中的视觉不确定性

本文探讨了视觉不确定性作为对使用生成对抗网络 (GAN) 创建的艺术品的描述。视觉不确定性描述的图像似乎描绘了真实场景，但经过仔细检查，无法连贯的空间解释。GAN 模型似乎倾向于产生不确定的图像，而不确定性是许多现代表现艺术以及大多数 GAN 艺术的一个关键特征。假设不确定性是强大但不​​完美的图像合成模型的结果，该模型必须结合对象、场景和纹理的一般类别。