# Learning fashion compatibility across apparel categories for outfit recommendation

arXiv: https://arxiv.org/abs/1905.03703 · HTML: https://mlsanity.com/paper/1905.03703/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2019.

## Built on by (most cited first, 5 of 5)

- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [SAT: Self-adaptive training for fashion compatibility prediction](https://mlsanity.com/paper/2206.12622/) (2022)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)
- [FashionFlow: Leveraging Diffusion Models for Dynamic Fashion Video Synthesis from Static Imagery](https://mlsanity.com/paper/2310.00106/) (2023)
- [Outfit Generation and Recommendation -- An Experimental Study](https://mlsanity.com/paper/2211.16353/) (2022)

## Abstract (arXiv)

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.