Fashion Compatibility Prediction Using Ensemble Learning

Fashion is important both financially and for self-expression. There are many tasks in the fashion domain which can be addressed with artificial intelligence. The task of fashion compatibility prediction is to determine how well a set of items work together to form an outfit. Two main tasks are typically used to evaluate the performance of a fashion compatibility prediction model – Outfit Compatibility Prediction and Fill in the Blank. In this work, a compatibility prediction model, which is based on the graph autoencoder, is evaluated. This same model is then used in a homogeneous ensemble learning approach, proposed to improve the compatibility prediction performance. This ensemble learning approach does not outperform the baseline. Finally, several potential approaches are introduced which may be of interest to future researchers