# Semi-supervised Compatibility Learning Across Categories for Clothing Matching

## TL;DR

This paper introduces a semi-supervised approach for learning cross-category clothing compatibility by aligning distribution structures with minimal annotated data, improving matching accuracy in fashion analysis.

## Contribution

It proposes a novel semi-supervised method that combines distribution alignment via adversarial learning with limited annotated data for better clothing compatibility modeling.

## Key findings

- Effective in aligning category distributions with minimal supervision
- Improves clothing matching accuracy on real-world datasets
- Utilizes adversarial learning to enhance compatibility learning

## Abstract

Learning the compatibility between fashion items across categories is a key task in fashion analysis, which can decode the secret of clothing matching. The main idea of this task is to map items into a latent style space where compatible items stay close. Previous works try to build such a transformation by minimizing the distances between annotated compatible items, which require massive item-level supervision. However, these annotated data are expensive to obtain and hard to cover the numerous items with various styles in real applications. In such cases, these supervised methods fail to achieve satisfactory performances. In this work, we propose a semi-supervised method to learn the compatibility across categories. We observe that the distributions of different categories have intrinsic similar structures. Accordingly, the better distributions align, the closer compatible items across these categories become. To achieve the alignment, we minimize the distances between distributions with unsupervised adversarial learning, and also the distances between some annotated compatible items which play the role of anchor points to help align. Experimental results on two real-world datasets demonstrate the effectiveness of our method.