Given an outfit, what small changes would most improve its fashionability?
Facebook introduced Fashion++, an approach that proposes minimal adjustments to a full-body clothing outfit that will have maximal impact on its fashionability.
The AI system uses a deep image-generation neural network to recognize garments and offer suggestions on what to remove, add, or swap. It can also recommend ways to adjust a piece of clothing, such as tucking in a shirt or rolling up the sleeves.
Fashion++ focuses specifically on minimal edits, suggesting adjustments that are more realistic and practical than buying an entirely new outfit. The system uses a discriminative fashionability classifier that is trained on thousands of publicly available images of outfits that have been judged to be stylish. These serve as ground truth examples of fashionable outfits, and unfashionable examples are then bootstrapped by swapping garments on the fashionable examples with their least similar counterparts.
Once the classifier is trained, the system gradually updates the outfit in order to make it more fashionable. An image-generation neural network renders the newly adjusted look, using a variational auto-encoder to generate the silhouette and a conditional generative adversarial network (cGAN) to generate the color and pattern. The latent encodings learned by this generator are further used to identify which garments in its inventory will best achieve the style.
Experiments show that the system’s recommendations bring images closer to ground truth examples and that human evaluators find the Fashion++ suggestions not only fashionable but also easy to implement.
Fashion++ is an example of how AI can be useful in a domain such as fashion, which some might think would be too creative or subjective for these systems. Rather than dictating or redefining what is fashionable, Fashion++ learns from examples in order to offer practical fashion advice.