Neural Compatibility Modeling with Attentive Knowledge Distillation
Neural Compatibility Modeling with Attentive Knowledge Distillation
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DOI：
10.1145/3209978.3209996
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Xuemeng Song;Fuli Feng;Xianjing Han;Xin Yang;W. Liu;Liqiang Nie
中科院分区：
文献类型：
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作者：
Xuemeng Song;Fuli Feng;Xianjing Han;Xin Yang;W. Liu;Liqiang Nie
最近，蓬勃发展的时尚行业及其巨大的潜在利益引起了许多研究界的极大关注。特别是，越来越多的研究工作致力于互补服装匹配，因为匹配衣服以制作合适的服装已经成为许多人，特别是那些没有审美感的人的日常头痛。由于神经网络在图像分类和语音识别等各种应用中取得了显着的成功，研究人员能够采用数据驱动的学习方法来分析时尚物品。然而，现有的研究忽略了服装领域积累的丰富的有价值的知识（规则），特别是服装搭配的规则。为此，在这项工作中，我们通过整合先进的深度神经网络和丰富的时尚领域知识，阐明了互补的服装匹配。考虑到规则具有模糊性，不同的规则对不同的样本具有不同的置信度，提出了一种基于师生网络的注意知识提取的神经相容性建模方案。在真实世界数据集上的大量实验表明，我们的模型优于几种最先进的方法。基于比较，我们观察到某些时尚见解，可以增加价值的时尚匹配研究。作为一个副产品，我们发布了代码，并涉及参数，以造福其他研究人员。
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 the 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 the 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 methods. Based upon the comparisons, we observe certain fashion insights that can add value to the fashion matching study. As a byproduct, we released the codes, and involved parameters to benefit other researchers.