  We consider grading a fashion outfit for recommendation, where we assume that
users have a closet of items and we aim at producing a score for an arbitrary
combination of items in the closet. The challenge in outfit grading is that the
input to the system is a bag of item pictures that are unordered and vary in
size. We build a deep neural network-based system that can take variable-length
items and predict a score. We collect a large number of outfits from a popular
fashion sharing website, Polyvore, and evaluate the performance of our grading
system. We compare our model with a random-choice baseline, both on the
traditional classification evaluation and on people's judgment using a
crowdsourcing platform. With over 84% in classification accuracy and 91%
matching ratio to human annotators, our model can reliably grade the quality of
an outfit. We also build an outfit recommender on top of our grader to
demonstrate the practical application of our model for a personal closet
assistant.

Computer Vision and Pattern Recognition