I did not directly work on this project, but I am proud of the way Sunroof uses 3D aerial imagery (the same 3D imagery we use in Earth view in maps.google.com, my previous project) to estimate solar incidence on roofs.
For the last few weeks, Googlers have been obsessed with an internal visualization tool that Alexander Mordvintsev in our Zurich office created to help us visually understand some of the things happening inside our deep neural networks for computer vision. The tool essentially starts with an image, runs the model forwards and backwards, and then makes adjustments to the starting image in weird and magnificent ways.
In the same way that when you are staring at clouds, and you can convince yourself that some part of the cloud looks like a head, maybe with some ears, and then your mind starts to reinforce that opinion, by seeing even more parts that fit that story ("wow, now I even see arms and a leg!"), the optimization process works in a similar manner, reinforcing what it thinks it is seeing. Since the model is very deep, we can tap into it at various levels and get all kinds of remarkable effects.
Alexander, , and Mike Tyka wrote up a very nice blog post describing how this works:
There's also a bigger album of more of these pictures linked from the blog post:
I just picked a few of my favorites here.
- StanfordComputer Science, B.S., 1999 - 2003
- Stanford UniversityComputer Science, M.S., 2003 - 2004
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- MicrosoftSoftware Engineer Intern, 2003 - 2003
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