论文标题
SAT：时尚兼容性预测的自适应培训
SAT: Self-adaptive training for fashion compatibility prediction
论文作者
论文摘要
本文为时尚兼容性预测提供了自适应培训（SAT）模型。它着重于学习一些硬件，例如具有相似颜色，纹理和图案功能的项目，但由于美学或时间变化而被认为是不兼容的。具体来说，我们首先设计了一种定义硬服装的方法，并根据建议为其推荐项目的困难定义并分配了难度分数（DS）（DS）。然后，我们提出了一个自适应三胞胎损失（SATL），其中考虑了服装的DS。最后，我们提出了一个非常简单的条件相似性网络，将提出的SATL结合在一起，以在时尚兼容性预测中学习硬件。公开可用的多货车和多vore Offits-D数据集的实验证明了我们SAT在时尚兼容性预测中的有效性。此外，我们的SATL可以很容易地扩展到其他条件相似性网络以提高其性能。
This paper presents a self-adaptive training (SAT) model for fashion compatibility prediction. It focuses on the learning of some hard items, such as those that share similar color, texture, and pattern features but are considered incompatible due to the aesthetics or temporal shifts. Specifically, we first design a method to define hard outfits and a difficulty score (DS) is defined and assigned to each outfit based on the difficulty in recommending an item for it. Then, we propose a self-adaptive triplet loss (SATL), where the DS of the outfit is considered. Finally, we propose a very simple conditional similarity network combining the proposed SATL to achieve the learning of hard items in the fashion compatibility prediction. Experiments on the publicly available Polyvore Outfits and Polyvore Outfits-D datasets demonstrate our SAT's effectiveness in fashion compatibility prediction. Besides, our SATL can be easily extended to other conditional similarity networks to improve their performance.
