  Modern machine learning techniques, such as deep neural networks, are
transforming many disciplines ranging from image recognition to language
understanding, by uncovering patterns in big data and making accurate
predictions. They have also shown promising results for synthesizing new
designs, which is crucial for creating products and enabling innovation.
Generative models, including generative adversarial networks (GANs), have
proven to be effective for design synthesis with applications ranging from
product design to metamaterial design. These automated computational design
methods can support human designers, who typically create designs by a
time-consuming process of iteratively exploring ideas using experience and
heuristics. However, there are still challenges remaining in automatically
synthesizing `creative' designs. GAN models, however, are not capable of
generating unique designs, a key to innovation and a major gap in AI-based
design automation applications. This paper proposes an automated method, named
CreativeGAN, for generating novel designs. It does so by identifying components
that make a design unique and modifying a GAN model such that it becomes more
likely to generate designs with identified unique components. The method
combines state-of-art novelty detection, segmentation, novelty localization,
rewriting, and generative models for creative design synthesis. Using a dataset
of bicycle designs, we demonstrate that the method can create new bicycle
designs with unique frames and handles, and generalize rare novelties to a
broad set of designs. Our automated method requires no human intervention and
demonstrates a way to rethink creative design synthesis and exploration.
