  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.
