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Learning Fashion Compatibility Across Apparel Categories for Outfit Recommendation

机译：学习跨服装类别的时尚兼容性以推荐服装

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摘要

This paper addresses the problem of generating recommendations for completing the outfit given that a user is interested in a particular apparel item. The proposed method is based on a siamese network used for feature extraction followed by a fully-connected network used for learning a fashion compatibility metric. The embeddings generated by the siamese network are augmented with color histogram features motivated by the important role that color plays in determining fashion compatibility. The training of the network is formulated as a maximum a posteriori (MAP) problem where Laplacian distributions are assumed for the filters of the siamese network to promote sparsity and matrix-variate normal distributions are assumed for the weights of the metric network to efficiently exploit correlations between the input units of each fully-connected layer.
机译：这篇文章解决了在用户对特定服装项目感兴趣的情况下生成建议以完成服装的问题。所提出的方法基于用于特征提取的暹罗网络，然后基于用于学习时尚兼容性度量的全连接网络。暹罗网络生成的嵌入物增加了颜色直方图特征，这些特征是颜色在确定时尚兼容性方面的重要作用所激发的。网络的训练被公式化为最大后验（MAP）问题，其中为暹罗网络的滤波器假定拉普拉斯分布以促进稀疏性，为度量网络的权重假定矩阵变量正态分布以有效利用相关性在每个完全连接层的输入单元之间。

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