We perform a set of experiments to demonstrate that images generated using a Generative Adversarial Network can be modified using 'semiotics.' We show that just as physical attributes such as the hue and saturation of an image can be modified, so too can its non-physical, abstract properties using our method. For example, the design of a flight attendant's uniform may be modified to look more 'alert,' less 'austere,' or more 'practical.' The form of a house can be modified to appear more 'futuristic,' a car more 'friendly' a pair of sneakers, 'evil.' Our method uncovers latent visual iconography associated with the semiotic property of interest, enabling a process of visual form-finding using abstract concepts. Our approach is iterative and allows control over the degree of attribute presence and can be used to aid the design process to yield emergent visual concepts.

翻译：我们通过一系列实验证明，利用生成对抗网络生成的图像可借助“符号学”进行修改。研究表明，如同物理属性（如色调与饱和度）可被调整一样，图像的非物理抽象属性也可通过我们的方法实现修改。例如，空乘制服设计可被调整为更显“警觉”、减少“肃穆感”或更具“实用性”；房屋形态可呈现更“未来感”的效果，汽车可变得更具“亲和力”，而一双运动鞋则可被赋予“邪恶”气质。我们的方法揭示了与目标符号学属性相关的潜在视觉图像学，实现了通过抽象概念进行视觉形态探索的过程。该方法具有迭代特性，允许对属性存在程度进行控制，并可辅助设计过程以催生涌现性视觉概念。