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Tensorflow implementation of Adversarial Autoencoders (github.com)
64 points by conan7882 2849 days ago
3 comments
polynomial 2849 days ago
For this particular implementation, what are the advantages of using it for supervised or unsupervised learning? (in general?)

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?

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locuscoeruleus 2848 days ago
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?
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amelius 2848 days ago
Question: what framework is used mostly by academic researchers these days for DL?
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nabla9 2848 days ago
Tensorflow, although PyTorch is gaining popularity.
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amelius 2848 days ago
Ok. I guess in this case the authors used TF because three of the authors work at Google.
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jingleheimer 2849 days ago
This code is really good.
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AstralStorm 2848 days ago
And impossible to use thanks to no license information.
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edtruji 2848 days ago
MIT License, it was added after your post https://github.com/conan7882/adversarial-autoencoders-tf/blo...
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Lorkki 2848 days ago
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.

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conan7882 2846 days ago
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.
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