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.

本文探讨了生成对抗网络的应用在时尚设计上的可能性。实验表明, 基于属性的GAN（AttGAN）可以成功编辑时装的视觉特性，但也提示着GAN在设计概念的属性级别编辑上所存在的一些关键限制和未来需要解决的研究问题。

利用生成对抗网络进行服装设计