Recently, the booming fashion industry and the huge potential benefit has attracted tremendous attention from many research communities. In particular, the problem of complementary clothing matching has gained increasing research efforts as matching clothes to make a suitable outfit has become a daily headache of many people, especially those who do not have a good taste of aesthetics. Thanks to the remarkable success of neural networks in various applications such as image classification and speech recognition, most of existing works adopt this pure data-driven learning method to analyze fashion items. Apparently, existing works overlook the rich valuable knowledge (rules) accumulated in fashion domain, especially the rules regarding clothing matching. To bridge this research gap, in this work, we aim to tackle the problem of complementary clothing matching by integrating the advanced deep neural networks and the rich fashion domain knowledge. In particular, considering that the rules can be fuzzy and different rules may have different rule confidence to different samples, we propose a neural compatibility modeling scheme with attentive knowledge distillation based on a teacher-student network scheme. Extensive experiments on real world dataset show the superiority of our model over the state-of-the-art methods, based on which we also provide certain fashion insights that can benefit the future research.
Figure 3: Illustration of the proposed scheme. The student network, consisting of dual-path neural networks, aims to learn the latent compatibility space where the implicit preference among items can be modeled via Bayesian Personalized Ranking. The teacher network encodes the domain knowledge and guide the student network via attentive knowledge distillation. t: top, b: bottom. “->”: category hierarchy. “>”: pair-wise preference. “no”: negative rules. The width of the arrows originated from rules refers to the rule confidence.