Jeff Wilke, CEO, Amazon Worldwide Consumer (AFP)
Jeff Wilke, CEO, Amazon Worldwide Consumer (AFP)

Amazon StyleSnap to tell you where you can buy that shirt you saw on Instagram

  • Shoppers "struggle to find styles they can't describe in words," Jeff Wilke, Amazon's CEO of consumer business said while introducing StyleSnap
  • When providing recommendations, StyleSnap considers a variety of factors such as brand, price range, and customer reviews

Las Vegas: Amazon.com Inc on Wednesday introduced "StyleSnap", a feature on its app that allows users to upload a picture of a look or style they like and get recommendations for similar items on the platform.

When providing recommendations, StyleSnap considers a variety of factors such as brand, price range, and customer reviews, the company said in a blog post.

Shoppers "struggle to find styles they can't describe in words," Jeff Wilke, Amazon's CEO of consumer business, said while introducing the service at the company's "re:MARS" conference on artificial intelligence in Las Vegas.

Where to find StyleSnap

The service will soon be embedded within the Amazon app. To get started, simply head over to the app and click the camera icon in the upper right hand. Then select the “StyleSnap" option and upload a photograph or screenshot of a fashion look that you like. StyleSnap will present you with recommendations for similar items on Amazon that match the look in the photo.

Amazon says building this feature was "no easy feat". Lifestyle images and influencer posts are unpredictable, with poses as varied as the locations.

How does it work

According to Amazon, StyleSnap uses computer vision and deep learning to identify apparel items in a photo, regardless of setting. Deep learning technology also helps classify the apparel items in the image into categories like “fit-and-flair dresses" or “flannel shirts."

Deep learning refers to a class of machine learning techniques based on artificial neural networks, which are inspired by the working of the human brain. Neural networks are made up of millions of artificial neurons connected to each other, and can be “trained" to detect images of outfits by feeding it a series of images.

To have neural networks identify a greater number of classes, several layers can be stacked on top of each other. The first few layers typically learn concepts such as edges and colours, while the middle layers identify patterns such as “floral" or “denim". After having passed through all of the layers, the algorithm can accurately identify concepts like fit and outfit style in an image. This, however, has its shortcomings—feed-forward neural networks stall and eventually degrade after a certain number of layers have been added.

Amazon uses residual networks to overcome this problem, as they use shortcuts to allow the training signal to skip over some of the layers in the network.

With inputs from Reuters.

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