Toward AI fashion design: An Attribute-GAN model for clothing match

Dressing in clothes based on the matching rules of color, texture, shape, etc., can have a major impact on perception, including making people appear taller or thinner, as well as exhibiting personal style. Unlike the extant fashion mining literature, in which style is usually classified according to similarity, this paper investigates clothing match rules based on semantic attributes according to the generative adversarial network (GAN) model. Specifically, we propose an Attribute-GAN to generate clothing-match pairs automatically. The core of Attribute-GAN constitutes training a generator, supervised by an adversarial trained collocation discriminator and attribute discriminator. To implement the Attributed-GAN, we built a large-scale outfit dataset by ourselves and annotated clothing attributes manually. Extensive experimental results confirm the effectiveness of our proposed method in comparison to several state-of-the-art methods.