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This article has been updated for the latest in AI Styling for 2020 and was first published in October 2019 A question that we often get asked when attending events or explaining Intelistyle for the first time to people is “Is this the real thing. Is it real AI styling?”. Which used to surprise us but then we realised that there are so many companies out there that claim to do Machine Learning, but what they really do is package up a couple of Amazon, Google or Microsoft cloud ML APIs, combine them with a freely available dataset, and voila, here’s a service that they can sell to customers as AI styling. So we decided to do a write up on our technology to help our customers understand how this really works.

How We Collect Our AI Styling Data Set

We constantly crawl the web, very much like google’s search engine does. Instead of indexing generic information though, we focus on fashion data. We have particular data sources that we prefer, like fashion magazines, social networking websites, retail websites, editorial fashion platforms and blogs. That process allows us to collect thousands of outfits put together by human stylists. We use images and text to get the most complete and accurate information. Now as you can probably imagine most of the web’s images are quite noisy. How do you extract the individual garments that are included in an outfit with varied backgrounds, different poses and models? The approach we took was to create a bounding box model that can create bounding boxes for each garment. Using that approach we were able to create a unique dataset of millions of outfits that we could use for training our AI styling model.

How We Keep Our AI Styling Data Set Up to Date

That dataset is constantly updated and quality controlled by our team. That allows us to keep up with the latest trends across different regions. We have clients in China, Europe and the Middle East and as you can imagine, the trends in each of these regions are very different. What is considered fashionable in one region, isn’t necessarily in another. Our Machine Learning team uses the latest academic research to craft a proprietary, bespoke set of AI models that analyse images and text. Each garment in our database is described using a 128-dimension “signature” or embedding. You can think of this as a very similar process to what Shazaam does for music tracks. Each of these signatures describes the important characteristics of each garment and leaves out the noise. However to create an AI styling intelligence that can perform as well as actual stylists, a good dataset and embeddings was not enough. While talking to our clients we realised that there are fashion rules that can make or break an outfit. For example, an off the shoulder top with puff sleeves should not be styled with a skinny-fit blazer. Our model could not predict these rules as good as humans yet. 

How Our AI Styling Quality Outperforms Human Stylists

The solution to that was to train another model that can detect rich attributes such as it’s fabric, cut, style, colour as well as other unique characteristics and categories for each garment of our dataset.  We also work with stylists with experience in brands such as M&S, Topshop, Prada and Vogue to create a unique set of ‘guidelines’ for our model to give preference to specific attributes when creating an outfit. And of course, because no two regions are the same, we can customise those guidelines to particular trends. For example, in the Middle East shorter hemlines are always paired with a longer overcoat and in Asia slip dresses should be layered over shirts. The result? A proprietary set of AI and data, that outperforms all published academic research to deliver outstanding styling quality, trusted by the world’s top luxury brands such as D&G, MaxMara and Lane Crawford.  We even tested it against real stylists and fashion influencers at London Fashion Week. As Forbes reported, 70% of respondents unwittingly chose the looks created by our model. 

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“Ones to Watch” list showcases 50 UK-based projects praised by experts for delivering new products, services and experiences through creative and tech expertise. This report, described as “who’s who guide of the UK companies at the forefront of Createch” was the highlight of the Createch 2019 conference held at London’s Code Node on 11^th June, as part of London Technology Week. The list was compiled by innovation research specialists Springwise with support from London & Partners and Digital Catapult.

Tim Davie, CEO of BBC Studios and Co-Chair of the CIC, a joint forum of government and industry said: “The intersection of creativity and technology is one of the most exciting growth areas in today’s economy. Createch 2019 will provide a unique guide for anyone trying to navigate through this fast-changing landscape.“

Createch has identified 4 key sectors that revolutionize user experience:

1) Immersive Entertainment

2) Transformative Experiences

3) Seamless Service

4) Personalised Tools

Intelistyle, AI styling and omnichannel personalisation start-up, is one of the 50 companies featured in the “Ones to Watch” list. “Styled by AI” isn’t yet a commonly used term, although that may be about to change, the report concludes. 

Intelistyle features in the “Personalised Tools VR ⁄ 3D ⁄ Robotics” category both for its B2B personalisation services to fashion retailers and for the personalisation offered to the end-user through its chatbot. The artificial intelligence (AI) software basically offers effective styling results across platforms: from in-store to online, even at home – a truly omnichannel experience.

Another reason Intelistyle made the list is the revolutionary technology that outperforms latest academic research to ensure excellent styling quality: Through the use of machine learning, the data from thousands of outfits put together by stylists, influencers, designers and retailers is extracted to decode the ‘essence of style’ and offer styling solutions.

The same exceptional styling quality got Intelistyle featured in Forbes, for outperforming human stylists during London Fashion Week in 2019.

Intelistyle’s “secret power” is providing customised, on-brand, personalised recommendations to fashion retailers, improving their KPIs (revenue, conversion, clicks-through-rate, average order value and more) while delivering a better customer experience.

Intelistyle in Createch: You can read the full report here.

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AI Stylist Outperformed Fashion Influencers

In February 2019, after training and testing our AI Stylist for 2 years we decided we were ready for London Fashion Week. We previously spent a lot of time testing with various stylists we worked with, as well as by withholding parts of the dataset from the model and measuring user behaviour in our app. We were itching though to take it to the ultimate fashion event of the industry and see the reactions of random members of the public.

Our goal was not to run a scientific experiment. This happens daily in our lab, where we test with thousands of outfits (see how ai fashion styling works). Our goal was to interact with real people, hear their feedback, see their reactions and most importantly satisfy our curiosity. Would fashion lovers from all over the world be able to tell the difference between an AI and a human stylist?

AI Stylist vs Human Stylists

So we decided to design an experiment. We generated 10 sets of outfits. In each set, we had two outfits of similar style: One generated by our AI stylist and the other created by Instagram fashion influencers.

The question we asked participants, was “Which outfit was created by an influencer?”. Here is an example.

We used SurveyMonkey to run the survey. Our sample consisted of 27 participants, 82% of whom were female. Each correct guess got 1 point. Participants scored on average 46%. For reference, if they were randomly responding to the questions they would have scored 50%. The highest score by a participant was 80% and the lowest 10%.

Often participants asked what were the criteria to make the decision and our answer was “Choose the outfit you like best”. 70% of them prefered the outfits created by our AI Stylist.

How the AI Stylist Works

So how do we do this? We examine thousands of outfits put together by human stylists, influencers, designers and retailers. Through deep learning, we extract the essence of style. We go a lot further than your typical computer vision techniques that focus on pattern recognition. Using machine learning to find visually similar clothes is commonplace these days.

However, style is a lot more nuanced than finding similar patterns or colours. Two seemingly different patterns or fabrics can be combined together to build a stunning finished look. And two pieces of clothing that would normally clash, can be brought together in a beautiful outfit with the use of right accessories. Employing the latest deep learning technology, we have just started decoding the genome of style which enables us to assign a unique signature to every piece of clothing. AI is only at the beginning of its journey but it’s making huge leaps every day.

Why is this important? Well, it is a huge step for diversity in the fashion retail industry – both luxury and fast fashion retailers are putting inclusion to the forefront of their offering. While body positivity is a huge movement, most retailers still build their lookbooks through studio photoshoots with fashion models. This means that looks offered to customers on retail websites cater to one body type, skin tone, hair tone and eye colour. Offering styling advice to each customer is expensive for a retailer. And what about different occasions, weather and style preferences?

AI offers the potential to scale fashion styling advice, creating outfits personal to the needs of each customer, celebrating diversity and individuality. Companies such as Stitchfix have already demonstrated the power of that approach. We want to take this further, allowing customers to digitise their wardrobe, style their own clothes and receive a personal service across their favourite retailers.

Read the full Forbes article on the success of our AI Stylist at London Fashion Week.

Interested in learning more? Get In Touch!

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