Tensorflow implementation of Adversarial Autoencoders(github.com)
github.com
Tensorflow implementation of Adversarial Autoencoders
https://github.com/conan7882/adversarial-autoencoders-tf
10 comments
From what I can gather the supervised approach allows you to only learn the representation for style rather than which digit it is. The only reason to use one over the other is to demonstrate that it works, I guess?
This code is really good.
And impossible to use thanks to no license information.
MIT License, it was added after your post
https://github.com/conan7882/adversarial-autoencoders-tf/blo...
Though, in the finest academic tradition, once you try to actually run it, you'll find that it silently depends on a separate library written by the author, which you'll have to find yourself.
(In this case "tensorcv", which is in a separate repository: https://github.com/conan7882/DeepVision-tensorflow )
That aside, I agree that it's an easier read than most ML code.
(In this case "tensorcv", which is in a separate repository: https://github.com/conan7882/DeepVision-tensorflow )
That aside, I agree that it's an easier read than most ML code.
I though I have removed all the dependency on 'tensorcv'. It turns out I forgot the dataflow part. Now it should be run without 'tensorcv'. Thanks for pointing out.
And if it's just being used for the MNIST dataset, is there a particular reason for using it in one or the other fashion?