  The designers' tendency to adhere to a specific mental set and heavy
emotional investment in their initial ideas often hinder their ability to
innovate during the design thinking and ideation process. In the fashion
industry, in particular, the growing diversity of customers' needs, the intense
global competition, and the shrinking time-to-market (a.k.a., "fast fashion")
further exacerbate this challenge for designers. Recent advances in deep
generative models have created new possibilities to overcome the cognitive
obstacles of designers through automated generation and/or editing of design
concepts. This paper explores the capabilities of generative adversarial
networks (GAN) for automated attribute-level editing of design concepts.
Specifically, attribute GAN (AttGAN)---a generative model proven successful for
attribute editing of human faces---is utilized for automated editing of the
visual attributes of garments and tested on a large fashion dataset. The
experiments support the hypothesized potentials of GAN for attribute-level
editing of design concepts, and underscore several key limitations and research
questions to be addressed in future work.
