We're going to build a variational autoencoder capable of generating novel images after being trained on a collection of images. We'll be using handwritten digit images as training data. Then we'll both generate new digits and plot out the learned embeddings. And I introduce Bayesian theory for the first time in this series :)
Code for this video:
https://github.com/llSourcell/how_to_...
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2 things
-The embedding visualization at the end would be more spread out if i trained it for more epochs (50 is recommended) but i just used 5.
-The code in the video doesn't fully implement the reparameterization trick (to save space) but check the GitHub repo for details on that.
More Learning resources:
https://jaan.io/what-is-variational-a...
http://kvfrans.com/variational-autoen...
http://blog.fastforwardlabs.com/2016/...
http://blog.fastforwardlabs.com/2016/...
http://blog.evjang.com/2016/11/tutori...
https://jmetzen.github.io/2015-11-27/...
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java sdk How to Generate Images - Intro to Deep Learning #14 | |
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| Education | Upload TimePublished on 14 Apr 2017 |
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