Abstract: 

Visual search is a rapidly emerging trend that is ideal for retail segments, such as fashion and home design, because they are largely driven by visual content, and style is often difficult to describe using text search alone. Visual search allows you to replace your keyboard with your camera phone by using images instead of text to search for things. Many people believe that visual search will change the way we search, as evidenced by the following quote from Pinterest co-founder and CEO Ben Silbermann in a CNBC interview, “A lot of the future of search is going to be about pictures instead of keywords.”

Through a technique called distance metric learning, a neural network can transform any image into a compact, information rich vector of numbers. In this tutorial/session, you will hear from visual search experts at Clarifai, eBay, Wayfair, and Walmart Labs/Jet.com. We’ll look at how you can use distance metric learning for visual similarity search within massive product catalogs – up to 1.1 billion items in eBay’s case.

If you are part of an in-house team of experts in machine learning and data science, you will learn:

* The latest state-of-the art visual search research and techniques as the speakers will share their in-depth knowledge on the subject

* How to scale your visual search solution to address the billion-scale problem

* How to train models that provide more specific and accurate results for visually rich categories
If you don’t have a team of in-house machine learning or data science experts but are interested in implementing visual search, you will learn about a solution that:

* Allows you to leverage without having to do any training our your dataset

* For those who want to train your own custom models, makes it easy to do so using less than 10 data examples using minimal code and no special infrastructure

Bio: