  Finding clothes that fit is a hot topic in the e-commerce fashion industry.
Most approaches addressing this problem are based on statistical methods
relying on historical data of articles purchased and returned to the store.
Such approaches suffer from the cold start problem for the thousands of
articles appearing on the shopping platforms every day, for which no prior
purchase history is available. We propose to employ visual data to infer size
and fit characteristics of fashion articles. We introduce SizeNet, a
weakly-supervised teacher-student training framework that leverages the power
of statistical models combined with the rich visual information from article
images to learn visual cues for size and fit characteristics, capable of
tackling the challenging cold start problem. Detailed experiments are performed
on thousands of textile garments, including dresses, trousers, knitwear, tops,
etc. from hundreds of different brands.
