$c^{+}$GAN: Complementary Fashion Item Recommendation

Abstract

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.

KDD'19: Workshop on AI for Fashion. arXiv admin note: text overlap with arXiv:1806.08977, arXiv:1411.1784, arXiv:1609.04802 by other authors

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• Conditional Generative Adversarial Nets
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• Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation
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Cited by in corpus (3)

• A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks
• Semi-supervised Adversarial Learning for Complementary Item Recommendation
• Exploiting Latent Codes: Interactive Fashion Product Generation, Similar Image Retrieval, and Cross-Category Recommendation using Variational Autoencoders