Via Technology Review

Artificial intelligence might just spawn a whole new style trend: call it “predictive fashion.”

In a paper published on the ArXiv, researchers from the University of California, San Diego, and Adobe have outlined a way for AI to not only learn a person’s style but create computer-generated images of items that match that style. The system could let retailers create personalized pieces of clothing, or could even be used to help predict broader fashion trends.

The paper details two different algorithms. First, the researchers trained a convolutional neural network (CNN) to learn and classify a user’s preferences for certain items, using purchase data scraped from Amazon in six categories: shoes, tops, and pants for both women and men. This type of recommender model is common in the online retail world, usually showing up in an “Other items you might like” area at the bottom of a page.

The team then used that information to train a generative adversarial network (GAN), a type of artificial intelligence that is especially proficient when it comes to generating realistic images. A GAN works by having two networks train on the same data. One of the networks generates fake images based on that data set, while the other network uses the same data to determine whether an image is real. This method lets the network improve its results. For this research, the GAN created multiple images of items for each user.

GANs, which were created by Ian Goodfellow, one of MIT Technology Review’s 35 Innovators Under 35 for 2017, have been in the news recently: after a different research team trained them on real images of Hollywood stars, the networks were able to create eerily believable fake celebrity faces. The faces weren’t all perfect, though—some had blurred areas or were missing features like eyebrows. There were fewer such problems with the fashion project, largely because the images used to train the networks were all shot from the same angle on white backgrounds, which makes generating convincing images much easier—something that would be essential if they were ever to be used to sell clothing.

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