Collocating Clothes With Generative Adversarial Networks Cosupervised by Categories and Attributes: A Multidiscriminator Framework.

The choice of which clothes to wear affects how one is perceived, as well as constitutes an expression of one's personal style. Based on the recent advances in image-to-image translation by the conditional generative adversarial network (cGAN), we propose a new framework with a multidiscriminator by incorporating different types of conditional information into the discriminator of cGAN for clothing matches. In contrast with most extant frameworks under cGAN, with one generator and one discriminator, the proposed framework investigates the potential of utilizing conditional information delivered by multidiscriminators to guide the generator. Under this framework, we propose an Attribute-GAN with two discriminators and a category-attribute GAN (CA-GAN) with three discriminators. In order to evaluate the performance of our proposed models, we built a large-scale data set that consists of 19,081 pairs of collocation clothing images with 90 manually labeled attributes. Experimental results demonstrate that with supervision of the additional attribute discriminator or category discriminator, the quality of the generated clothing images by GANs is consistently improved in comparison with the state-of-the-art methods.