In this paper, we formulate the outfit completion problem as a set retrieval task and propose a novel framework for solving this problem. The proposal includes a conditional set transformation architecture with deep neural networks and a compatibility-based regularization method. The proposed method utilizes a map with permutation-invariant for the input set and permutation-equivariant for the condition set. This allows retrieving a set that is compatible with the input set while reflecting the properties of the condition set. In addition, since this structure outputs the element of the output set in a single inference, it can achieve a scalable inference speed with respect to the cardinality of the output set. Experimental results on real data reveal that the proposed method outperforms existing approaches in terms of accuracy of the outfit completion task, condition satisfaction, and compatibility of completion results.

翻译：本文将该服装搭配补全问题建模为集合检索任务，并提出一种解决该问题的新型框架。该方案包含基于深度神经网络的条件集变换架构及兼容性正则化方法。所提方法利用对输入集具有置换不变性、对条件集具有置换等变性的映射函数，在反映条件集特性的同时，可检索出与输入集兼容的集合。此外，由于该结构通过单次推理即可输出集合元素，因此能实现对输出集基数的可扩展推理速度。真实数据集上的实验结果表明，本方法在服装搭配补全精度、条件满意度及补全结果兼容性方面均优于现有方法。