# R&D At Curalate: A Case Study of Deep Metric Embedding

DevFeed: [R&D At Curalate: A Case Study of Deep Metric Embedding](<https://devfeed.tech/articles/r-d-at-curalate-a-case-study-of-deep-metric-embedding-26522.md>)

Original publisher: [Read original article](<http://engineering.curalate.com/2018/02/01/deep-metric-embedding.html>)

Published: 2018-02-01T10:11:36Z

Content type: article

Language: en

Sources: [Curalate](<https://devfeed.tech/sources/curalate.md>)

Topics: [Computer vision](<https://devfeed.tech/topics/computer-vision.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [implementation](<https://devfeed.tech/topics/implementation.md>), [dataset](<https://devfeed.tech/topics/dataset.md>)

Tags: [case-study](<https://devfeed.tech/tags/case-study.md>), [computer](<https://devfeed.tech/tags/computer.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [deep](<https://devfeed.tech/tags/deep.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [e-commerce](<https://devfeed.tech/tags/e-commerce.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [learning](<https://devfeed.tech/tags/learning.md>), [machine](<https://devfeed.tech/tags/machine.md>), [metric](<https://devfeed.tech/tags/metric.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [research](<https://devfeed.tech/tags/research.md>), [vision](<https://devfeed.tech/tags/vision.md>)

## AI overview

A Curalate engineering case study describes building a visual search engine to identify clients' products in user-generated photos. It explains how a literature review led the team to use deep metric learning, which learns image embeddings that place images of the same product close together in Euclidean space.

## Source excerpt

At Curalate, we make social sell for hundreds of the world's largest brands and retailers. Our Fanreel product is a good example of this; it empowers brands to collect, curate, and publish social user-generated photos to their e-commerce site. A vital step in this pipeline is connecting the user generated content (UGC) to the product on our client's web site. Automating this process requires cutting edge computer vision techniques whose implementation details are not always clear, especially for production use cases. In this post, I review how we leveraged Curalate's R&D principles to build a visual search engine that identifies which of our clients' products are in user generated photos. The resulting system allows our clients to quickly connect user generated content to their e-comm site, enabling the UGC to generate revenue immediately upon distribution. Step 1: Do Your Homework We start every R&D project by hitting the books and catching up on the relevant research. This lets us understand what is feasible, the (rough) computational costs, and any pitfalls of various techniques. In this case, our goal is to find which products are in any UGC image using only the product images from the client's e-comm site. This is extremely difficult: UGC photos have dramatic lighting conditions, generally contain multiple objects or clutter, and may have undergone non rigid transformations (especially if it's a garment). Knowing we had a difficult problem on our hands, we did an extensive literature review on papers from leading computer vision conferences, journals, and even arxiv to ensure we have a good understanding of the state of the art. One approach stood out in the literature review: deep metric learning. Deep metric learning is a deep learning technique that learns an embedding function that, when applied to images of the same product, produces feature vectors that are close together in Euclidean space. This technique is perfect for our use case: we can train the sys