定制服装组合的可解释分区嵌入
Interpretable Partitioned Embedding for Customized Fashion Outfit Composition
摘要：
智能化的时装组合是近年来越来越流行的一种时尚。最近，一些基于深度学习的方法揭示了竞争性组合。然而，不可解释的特性使得这种基于深度学习的方法不能满足设计师、企业和消费者理解服装组成中不同属性的重要性的迫切需要。为了实现可解释的和定制的时装组合，我们提出了一个分区嵌入网络来学习服装项目的可解释表示。整个网络结构由三个部分组成：自动编码器模块，监督属性模块和多独立模块。自动编码器模块用于将所有有用的信息编码到嵌入中。在监督属性模块中，采用多个属性标签，以确保整体嵌入的不同部分对应不同的属性。在多独立模块中，采用对抗操作来实现相互独立的约束。利用可解释的、分块的嵌入，我们构造了一个服装组成图和一个属性匹配图。给定指定的属性描述，我们的模型可以推荐一个排名列表的服装组成与可解释的匹配分数。实验结果表明：（1）该模型所推荐的服装与已有的方法相比，具有更好的嵌入效果。
英文摘要：
Intelligent fashion outfit composition becomes more and more popular in these years. Some deep learning based approaches reveal competitive composition recently. However, the unexplainable characteristic makes such deep learning based approach cannot meet the the designer, businesses and consumers' urge to comprehend the importance of different attributes in an outfit composition. To realize interpretable and customized fashion outfit compositions, we propose a partitioned embedding network to learn interpretable representations from clothing items. The overall network architecture consists of three components: an auto-encoder module, a supervised attributes module and a multi-independent module. The auto-encoder module serves to encode all useful information into the embedding. In the supervised attributes module, multiple attributes labels are adopted to ensure that different parts of the overall embedding correspond to different attributes. In the multi-independent module, adversarial operation are adopted to fulfill the mutually independent constraint. With the interpretable and partitioned embedding, we then construct an outfit composition graph and an attribute matching map. Given specified attributes description, our model can recommend a ranked list of outfit composition with interpretable matching scores. Extensive experiments demonstrate that 1) the partitioned embedding have unmingled parts which corresponding to different attributes and 2) outfits recommended by our model are more desirable in comparison with the existing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
