GraphLab's deep learning - the power of graph applied to images
A couple of months ago we have released a deep learning toolkit for GraphLab Create. We just got a code contribution from Marian Moldovan & Enrique Otero, from Beeva.com, which utilizes GraphLab deep learning toolkit in a new and exciting ways.
Marian & Enrique created a super awesome application. Imagine you have a repository of images and you would like to understand the relation between the images. The images they used are of buildings in Barcelona, as this work was created at the hacknight of papis.io, a predictive API conference in Barcelona.
Here is the first building:
And here is the second building:
What is the architecture transition that can explain this path? Using GraphLab Create it is easy to compute!
In a nutshell, they first extracted images features using the deep learning toolkit. Then they used a nearest neighbor model to create a graph of all the similar buildings:
Next, they used the graph model to find the shortest path between two interesting buildings (number 16 and 23)
Marian & Enrique created a super awesome application. Imagine you have a repository of images and you would like to understand the relation between the images. The images they used are of buildings in Barcelona, as this work was created at the hacknight of papis.io, a predictive API conference in Barcelona.
Here is the first building:
And here is the second building:
What is the architecture transition that can explain this path? Using GraphLab Create it is easy to compute!
In a nutshell, they first extracted images features using the deep learning toolkit. Then they used a nearest neighbor model to create a graph of all the similar buildings:
Next, they used the graph model to find the shortest path between two interesting buildings (number 16 and 23)
| The Ipython notebook to reproduce this example is available here. |
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