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by distillpub on 2018-07-25 (UTC).

Differentiable Image Parameterizations - A new Distill article by @zzznah @nicolapezzotti @ludwigschubert & @ch402 https://t.co/48BaSim6bQ

A powerful, under-explored tool for neural network visualizations and art.

— distillpub (@distillpub) July 25, 2018
dataviztoolw_code
by ch402 on 2018-07-25 (UTC).

Big shout out to the (very cool!) similar work by @hiroharu_kato et al and @anishathalye et al!https://t.co/troZxrSRp3https://t.co/3LKdMb7HAD 4/

— Chris Olah (@ch402) July 25, 2018
research
by ludwigschubert on 2018-07-25 (UTC).

Here's that shared parameterization in action. Note how the visualizations are easier to compare when visual landmarks stay in place.https://t.co/FokUCWvQze pic.twitter.com/5zpF8f3wJ5

— Ludwig Schubert (@ludwigschubert) July 25, 2018
datavizw_code
by ludwigschubert on 2018-07-25 (UTC).

Style transfer used to mysteriously work best only on VGG architectures. See how a decorrelated image parameterization allowed us to bring naive style transfer (Only content + style loss after gram matrix transform a la Gatys et al.) to GoogLeNet:@zzznah @nicolapezzotti @ch402 pic.twitter.com/nS4OZn3YJd

— Ludwig Schubert (@ludwigschubert) July 25, 2018
datavizresearch

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