For those of you who liked the post I shared a couple of weeks ago about the images generated by neural nets (old post: https://plus.google.com/+JeffDean/posts/jVBUgDxhbRd), I'm happy to announce that Alexander Mordvintsev, , and Mike Tyka have put together an open-source iPython notebook containing the code that generates these images, and you can play around with it on your own images. (Note: this notebook depends on a few other packages, so you have to have enough persistence to install numpy and caffe to get this to work). The iPython notebook is at https://github.com/google/deepdream, but see the blog post linked to by this post for details.
The blog post asks that people tag images they generate and share with #deepdream , so I suspect you can keep looking at that tag to see all kinds of weird and wonderful images.
Have fun, everyone!
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.
I'm very happy Google commissioned this study, and I hope the city governments actually implement this.
Was in MTV last week, the Permanente Creek path needs improvement. Those SLOW gates are wrong; treat cyclists like adults, thank you very much, and do not threaten scofflaws with physical harm (car analogy: imagine "traffic calming" implemented with jersey barriers forcing you to swerve through a single-lane opening in the road). They block tandems, they block trailers, they impede bidirectional movement at safe speeds. The Charleston crossing needs to be fixed -- it is no less safe to hop curbs and medians than it is to ride down to an intersection (more cars from more directions), u-turn, and then right. There also needs to be signs indicating all the names of all the cross streets and where they might take you to -- uncertain cyclists don't like getting lost and going a mile out of their way by accident. Down near the entrance to Shoreline Park, there's a diversion around a maintenance area, it is too narrow and there is a blind curve, two of us from opposite directions gave each other a start ( https://youtu.be/NxZJNFpjRM0 ). That needs to be fixed. This stuff is all cheap, ought to be noncontroversial, and it would help.
By-the-way, around CAM, some of the more heavily traveled streets appear to have a summer rush-hour bike fraction of 25%. Anyone who thinks bikes are toys or bikes are for yuppies ought to think what it would mean to add 25% to Cambridge/Somerville rush hour traffic, or to add 100 cars to the parking garage here.
I won't be there, but if you'll be there, feel free to seek out these Google authors and ask them about their work.
- Google Senior Fellow, present
Prior to joining Google, I was at DEC/Compaq's Western Research Laboratory, where I worked on profiling tools, microprocessor architecture, and information retrieval. Prior to graduate school, I worked at the World Health Organization's Global Programme on AIDS, developing software for statistical modeling and forecasting of the HIV/AIDS pandemic.
I earned a B.S. in computer science and economics (summa cum laude) from the University of Minnesota and received a Ph.D. and a M.S. in computer science from the University of Washington. I was elected to the National Academy of Engineering in 2009, which recognized my work on "the science and engineering of large-scale distributed computer systems."
- University of WashingtonComputer Science
- University of MinnesotaComputer Science and Economics
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