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Tobias Weyand
Works at Google
Attended RWTH Aachen
Lives in Venice
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Tobias Weyand

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Android just got a whole lot smarter. This afternoon Google released new abilities for Now On Tap, a system built in to Android Marshmallow. Users can now use their smartphone’s camera to fin…
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I'm excited that I can finally share what I've been working on at Google! We just published our paper describing PlaNet, a Convolutional Neutral Network that can tell where in the world a photo was taken. From millions of geotagged photos PlaNet has learned which countries have fjords, where to find red phone booths, and which animals live on the Galapagos Islands. Check out our paper on arXiv below! A big thank you goes to my co-authors +Ilya Kostrikov and +James Philbin!
Abstract: Is it possible to build a system to determine the location where a photo was taken using just its pixels? In general, the problem seems exceptionally difficult: it is trivial to construct situations where no location can be inferred. Yet images often contain informative cues such as ...
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PlaNet sounds realy nice. I can suggest a great source of geolocated images here at www.mapillary.com/. Keep up the good work!
/D
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Turning black-and-white photographs into colour photographs with a bit of deep learning.
I really enjoyed this one - it is cool and nicely written. The author is the original designer of node.js. As far as I know he doesn't have a background in machine learning. I wonder if magic will soon be in every good programmer's toolbox?
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Some good laughs in here!
 
http://clickotron.com/
Articles generated by a recurrent neural network trained on Buzzfeed:
"How The World's Most Extreme Baby Moms Lost Weight"
"David Beckham Looking For Them In "Lady Box""
"This Guy Thinks His Cat Was Drunk For His Five Years, He Gets A Sex Assault At A Home"
"What To Use When You're A Great Person"
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Amazing results on novel view synthesis using deep learning... This is the paper http://arxiv.org/abs/1506.06825

(Via +Kosta Derpanis​)
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New Paper:

Fixing WTFs: Detecting Image Matches caused by Watermarks, Timestamps, and Frames in Internet Photos

http://www.vision.rwth-aachen.de/projects/fixing-wtfs
Detecting Image Matches caused by Watermarks, Timestamps, and Frames in Internet Photos. examples.png. Summary. A common problem of computer vision applications that are based on Internet photos are false-positive matches caused by Watermarks, Timestamps, and Frames (WTFs) superimposed on the ...
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WTF. Cool name :D
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Tobias Weyand

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Google's landmark recognition (and more!) is now available to everyone!
 
Now you can detect objects in your images the same way +Google Photos does. Learn how to get started with Cloud Vision API: http://goo.gl/iePJsA
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The next generation of applications won’t just store images. They’ll actually understand them thanks to models like the one we’re releasing today. Our Cloud Vision API uses machine learning to classify images into thousands of categories, detect objects (and facial features), and even identify language. We’re excited to see what you build with it: http://goo.gl/RfEFv0
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Machine learning has had a huge impact on computer science, making it possible explore new frontiers of research and to develop amazingly useful products that millions of people use every day. Our machine learning system, TensorFlow, is used for everything from speech recognition in the Google app, to Smart Reply in Inbox, to search in Google Photos. 

And now you can use it too.

Today, we’re proud to announce the open source release of TensorFlow - our second-generation machine learning system that is a general, flexible, portable, easy-to-use, and completely open source software library for numerical computation using data flow graphs. Head over to the Google Research blog to learn more.
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All of these images were computer generated!

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, +Christopher Olah, and Mike Tyka wrote up a very nice blog post describing how this works:

http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html

There's also a bigger album of more of these pictures linked from the blog post:

https://goo.gl/photos/fFcivHZ2CDhqCkZdA

I just picked a few of my favorites here.
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Have him in circles
168 people
Dan Romescu's profile photo
Henry Pan's profile photo
Stephan Jonas's profile photo
Tobias Baumgartner's profile photo
Andreas Kahler's profile photo
Arne Mauser's profile photo
Glailton Luis's profile photo
Friends of Nature's profile photo
Gibran Fuentes Pineda's profile photo
Education
  • RWTH Aachen
    2015
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  • Google
    present
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Currently
Venice
Previously
Aachen - Paris
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Sehr nett und super lecker! Unbedingt das Cornelius-Bräu probieren!
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