  Recently, the booming fashion sector and its huge potential benefits have
attracted tremendous attention from many research communities. In particular,
increasing research efforts have been dedicated to the complementary clothing
matching as matching clothes to make a suitable outfit has become a daily
headache for many people, especially those who do not have the sense of
aesthetics. Thanks to the remarkable success of neural networks in various
applications such as image classification and speech recognition, the
researchers are enabled to adopt the data-driven learning methods to analyze
fashion items. Nevertheless, existing studies overlook the rich valuable
knowledge (rules) accumulated in fashion domain, especially the rules regarding
clothing matching. Towards this end, in this work, we shed light on
complementary clothing matching by integrating the advanced deep neural
networks and the rich fashion domain knowledge. Considering that the rules can
be fuzzy and different rules may have different confidence levels to different
samples, we present a neural compatibility modeling scheme with attentive
knowledge distillation based on the teacher-student network scheme. Extensive
experiments on the real-world dataset show the superiority of our model over
several state-of-the-art baselines. Based upon the comparisons, we observe
certain fashion insights that add value to the fashion matching study. As a
byproduct, we released the codes, and involved parameters to benefit other
researchers.
