Learning from Designers: Fashion Compatibility Analysis Via Dataset Distillation

Learning fashion compatibility is of great significance to both academic research and industry, which serves as a key technique for many real applications like online shopping recommendation and clothing generation. In previous studies, user-generated data (e.g. outfits from social media platform) are usually used for learning item embeddings and further modeling the compatibility. However, due to the noisy and messy nature of such data, one can hardly learn a representation that can clearly characterize the fashion-related attributes (e.g. color, material). In this paper, we propose an Attention-based Dataset Distillation Graph Neural Network (ADD-GNN) to leverage the designer-generated data as a guidance on modeling the outfit compatibility. Specifically, we jointly optimize two components which distill knowledge from fashion designers for feature representation learning and model the overall compatibility through attention-based graph neural network. Experimental results on real world fashion datasets clearly demonstrate the superiority of our proposed ADD-GNN against several competitive baselines in outfit compatibility tasks, which proves the effectiveness of distilling knowledge from designers.