  We present a conditional generative adversarial model to draw realistic
samples from paired fashion clothing distribution and provide real samples to
pair with arbitrary fashion units. More concretely, given an image of a shirt,
obtained from a fashion magazine, a brochure or even any random click on ones
phone, we draw realistic samples from a parameterized conditional distribution
learned as a conditional generative adversarial network ($c^+$GAN) to generate
the possible pants which can go with the shirt. We start with a classical cGAN
model as proposed by Mirza and Osindero [arXiv:1411.1784] and modify both the
generator and discriminator to work on captured-in-the-wild data with no human
alignment. We gather a dataset from web crawled data, systematically develop a
method which counters the problems inherent to such data, and finally present
plausible results based on our technique. We propose simple ideas to evaluate
how these techniques can conquer the cognitive gap that exists when arbitrary
clothing articles need to be paired with another relevant article, based on
similarity of search results.
