Researchers from the University of Texas at Austin (UT-Austin), Cornell Tech, Georgia Tech, and Facebook AI Research have created an artificial intelligence system that can be used to give personalized fashion advice, published here. Now one of the projects under UT-Austin's Department of Computer Science, Fashion, was created with a philosophy of making minimal changes to maximize outfit fashionability. According to a press release from the UT-Austin, the system analyzes several factors of outfits, such as the color, pattern, texture, and shape, and gives recommendations based on this analysis. The researchers posted a video summarizing how Fashion works. A key component of how the system works is in machine learning.

Given an outfit pieced together from a limitless wardrobe, what nips and tucks might improve its overall stylishness? That's the question researchers at Cornell, Georgia Tech, and Facebook AI Research recently investigated in a research paper published on the preprint server Arxiv.org. In it, they describe an approach that aims to identify small adjustments to outfits that might have an outsized impact on fashionability. It brings to mind Amazon's Echo Look, a connected camera that combines human and machine intelligence to recommend styles, color-filter clothes, compare two outfits, and keep track of what's in personal wardrobes. But the researchers assert their techniques are more sophisticated than most.

We use customer demand data for fashion articles on Myntra, and derive a fashionability or style quotient, which represents customer demand for the stylistic content of a fashion article, decoupled with its commercials (price, offers, etc.). We demonstrate learning for assortment planning in fashion that would aim to keep a healthy mix of breadth and depth across various styles, and we show the relationship between a customer's perception of a style vs a merchandiser's catalogue of styles. We also backtest our method to calculate prediction errors in our style quotient and customer demand, and discuss various implications and findings.

We are already aware of machine learning codes disrupting the fashion retail market such as inventory management, virtual reality systems for apparel fitting and most commonly, recommendation systems based on consumer preferences, buying behavior, etc. I myself captured some of these in the post on ML applications in the e-commerce ecosystem, written a few months ago. Today, on a separate note, we'll look at some of the initiatives where machine learning is disrupting the consumer fashion market in some really novel and unique ways. In a way, these models are planning for a future where machines will be able to interpret art the way we do. Determining "Fashionability" Researchers at University of Toronto are working at building a machine learning model to help you improve your "fashionability".