5. Genetic algorithms. These are search techniques that look for approximate or exact solutions to optimisation problems. They are guided by a fitness function.
Smart Fashion: A simple AI fashion styling system
Generally, fashion styling involves the following seven steps: Choosing a theme, selecting a primary colour, mixing and matching clothing pieces, selecting accessories, model or client fitting, hair styling and makeup. AI computer modelling program usually includes the following functions: Representation of the garments computationally (focuses on fashion object), detection, tracking and forecasting fashion trends (about fashion process) and modelling human stylist behaviour (a mixture of both).
Smart Fashion is a machine learning application that recommends fashion looks by learning user preferences through Multilayer Perceptron model. The system scores users’ customised style looks based on fashion trends and users’ personal style history.
Smart Fashion system comprises five major components for fashion styling task:
Initialisation program, database, smart engine, learning components and user interface. It uses artificial neural networks to learn user preferences. It creates a database containing, say, 32 dresses and 20 shoes for four different events, encodes a standard style rules engine, generates more than 500 looks and ranks them by a final score in descending order. The score indicates how fashionable each look is based on users’ feedback. The learning component trains artificial neural network to learn users’ personal preferences and adjusts the final score. The system allows users to customise a fashion look and then evaluate it. This feature provides a shopping guide to inform users’ purchase plans.
Smart Fashion provides a numeric evaluation for user preferences. The system is initialised by the user taking a fashion personality questionnaire. The output of the questionnaire is stored in the system. So users can restart the program by clicking Reset button, avoiding the need to retake the quiz. The questionnaire is described in the box below.
For each style, the system has a set of default database and styling rules. Using style rules, the Smart Engine pairs up dress and shoes, assigns initial scores to each pair and creates a ‘pairs’ table in the database. Two main outputs of the system are: A recommender system to recommend users’ fashion looks based on user interactions and an evaluation system to evaluate users’ customised looks with a score that assists users’ shopping plans.
System learning predicts a pair’s score based on correlations between scores and item attributes. The dataset is prepared and pre-processed. For example, all the dresses and shoes are collected in typical standard style from various sources such as websites and designers’ collections. All clothing items and shoe items are assigned a number of attributes, which are compiled in tables. Standard rules are created for the database. Every rule interprets a binary (positive or negative) relationship between pairs of attributes. Boredom, one of the major factors in fashion model, is created in fashion styling for wearing the same looks several times. So, a count of ‘wear’ is added in the program as a parameter of boredom.
The output of the Smart Engine is a pair table for each event, which stores the pair data and score, and pair’s views, which stores ‘like’ and ‘dislike’ data. The data for ‘like’ and ‘dislike’ is created based on the learning components and evaluation based on the selected attributes. The learning task is completed using a number of Machine Learning algorithms and data preprocessing to predict the final score based on correlation with item attributes. Learning rate, a mathematical measure, is a decreasing function of time as the process is very inefficient in the beginning when we are learning to do something new. The efficiency gets better with more practice.
How AI is helping to increase the business
AI has two main applications in fashion: One is styling and creating clothes and the other is helping fashion business houses predict current and future trends to create clothes that are in high demand and enable a highly personalised shopping experience for customers.
AI can help retailers to leverage massive amounts of unstructured data to improve and personalise the online shopping experience. At present, fashion brands and retailers are dependent on a limited amount of data to forecast the products to order and then the products to discount or replenish towards the end of the season. A wrong prediction results in revenue loss due to mark-downs, wastage and discounted selling of items. AI can help a retailer to align product stocks with demand, and also display products for maximum sales generation by analysing large amounts of data—for example, browsing and shopping history of customers.
Stylumia is an AI-powered tool that can interpret a huge amount of data collected from videos, e-commerce sites, social media, etc. Its users have reported 200-400 per cent jump in revenue growth and about 25 per cent reduction in inventory. Fashion leaders such as Vero Moda, W, Jack & Jones and Pepe Jeans regularly use Stylumia to predict consumers’ likes and dislikes.
IBM’s Watson is partnering with The North Face to offer guided shopping online. The AI system asks shoppers questions on factors such as gender, time of year and product details to deliver customised recommendations.
Marchesa, a high-end French label, has created a cognitive dress using AI tools. The dress changes colour according to the live comments made by clients on their social media accounts.
The fashion industry is increasingly embracing the latest developments in AI to increase e-commerce productivity, improve retail experience, and provide promotional campaigns and clothes themselves. India is also waking up to AI-enabled fashion. Designers Falguni and Shane Peacock have unveiled a collection in collaboration with IBM-Watson. Myntra has launched a popular line of machine-designed t-shirts, called Moda Rapido, which have been developed using in-house AI platform RAPID. Vue.ai by Mad Street Den, an AI company in Bengaluru, provides personalised style recommendation to shoppers.
Image recognition apps such as Snap Fashion and ASAP54 are available to build search engines. Imagine a user taking a picture of someone wearing a dress that he has liked, or something as abstract as a painting, and the image-recognition search engine searching the huge database of products that can be bought to make available the desired or similar items. This online shopping experience could be overwhelming for customers.
As online shopping continues to grow, it is imperative for companies to utilise tools that can help them attract and retain customers. AI can serve as a communication platform to update customers on collection releases and product availability. It can provide interactive storytelling and unique digital experiences with the help of creative algorithms. Also, it can strengthen e-commerce by tracking users’ preferences and giving them a customised shopping experience.
An AI machine will crunch fashion data related to millennial, current colour and silhouette trends, trend forecasts for the next season, what movie stars are wearing and such similar information, and suggest in a matter of seconds several options to choose from. In comparison, a fashion designing team working manually will take many days to come up with such suggestions. In fact, some experts feel that artificial intelligence systems have now reached a stage where they could well replace the human fashion stylists!