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How Neural Networks Can Help Us
Expand Visual Creativity
Jason Toy
ABOUT ME
❖ builder of things
❖ SFPC
❖ somatic
ArtTech
DEEP DREAMING - INCEPTION
seeing what the computer sees
neural network concepts
❖ blackbox
❖ neurons-inputs;outputs; 0-1
❖ accelerates at processing images
❖ layer types
❖ training ~= compiling
❖ most ML problems:
classification,regression,reinforcement
deepdream key modifications
how do we understand neural network?
objective function: activate as many neurons in a layer
key trick: push back to image
looping and recursion
GOOGLENET
m
how we changed the objective function of deep dream
❖ https://github.com/somaticio/bat-country/blob/
master/batcountry/batcountry.py
STYLETRANSFER
neural style details
❖ classifier
❖ same network can be used (vgg, googlenet)
❖ visual representation of our world
❖ repurposed - transfer learning
high layers in the network act as the content of the image
style computed from multiple layers’ filter responses
Trials from experimentation
of modifying data and algorithms
other examples of image processing networks
❖ black and white photos -> colorized images
❖ low resolution -> super resolution
❖ blurry images -> clear images - CSI magic!!!
❖ image to story -> romance novels / ad generators
Ideas for other transformations?
❖ selective region of inception/style transfer?
❖ dreaming different objects
❖ mixing transformations
❖ really up to our imaginations!! - we will explore
hackathon projects
❖ style transfer photo gallery
❖ slack stylebot
❖ inceptionism - modify an objective function (advanced
programming and math)
Thank you!!
Questions?
Jason Toy
jason@somatic.io
http://somatic.io

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