Home Retail & Consumer How Deep Learning is Revolutionising Retail Sector

How Deep Learning is Revolutionising Retail Sector

Artificial intelligence is a hot topic across the globe. Self-driving vehicles and conversational interfaces boast as some of the most popular modern-day technologies.

According to reports issued by SLI Systems, at least 54% of retailers are already planning to add AI technologies to their toolkits, with 20% of them planning to integrate this technology within a year.

Advances in deep learning have increased the rate of adopting AI among retailers.

Deep Learning Defined
Deep learning can be described as a machine learning technique for developing and training modern neural networks.

Neural networks are software or hardware systems that mimic how neurons function in the brain. In deep learning, the network is first fed with enough data (samples), to enable it to make decisions with regard to what it already knows.

For example, such a network can differentiate one product from another such as differentiating shoes from a dress.

READ MORE: 10 Powerful Applications of Artificial Intelligence in Retail

The technique uses variations in the dark and light parts of a given image to determine how a shoe looks like.

Once done, it considers other aspects such as colors, angles, and shapes to understand how the item looks and then learn to point it out from other items.

Why Is Deep Learning Not Yet Popular In The Retail Sector?
Training networks is not an easy task. The aforementioned processes are complex and run behind the scenes in the network.

Dealing with a process such as dress recognition demands creating millions of large arrays of numbers. Data samples present another challenge.

For instance, neural networks have documented some mistakes such as undesirable correlations in data as reported by researchers from the famous University of Washington. Such shortcomings can lead to highly biased results.

Training an algorithm to point out different items will require between one thousand to five thousand images of such an object.

Each image has to contain accurate attributes and name of the item. Generating such images tends to be quite expensive.

For instance, 170,000 items will require around 1 billion precisely labelled images. Hiring someone to obtain them manually can cost around $0.20 for every image or $240 million for all the images.

Neuromation scientists have developed a solution to such challenges by generating 3D replicas of retail items with 100% labelling accuracy in the digital copies. More images can now be obtained by combining the objects under different angles, and lighting conditions.

Early Uses of the Deep Learning Technique
Susan Zoghbi, a researcher from KU Leuven, developed a technique that generates better results on image recognition.

This technique targets online fashion stores. In her most recent findings, both business owners and consumers have reported that some attributes of a garment are missing in an accompanying image.

Susan is now developing a search tool for handling the problem to ensure the images generated are more accurate such that they have matching textual descriptions and characteristics.

The good news is that neural networks will be powering this process.
Susan has also worked on teaching neural networks to link Pinterest pins to texture descriptions of relevant products.

For instance, she was able to match Pinterest products to similar items on Amazon.

Such technology can be utilized in recommendation engines to revolutionize how people discover retail products online.

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KC Cheung
KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
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