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Tensorflow implementation of Adversarial Autoencoders
- jingleheimer 8y agoThis code is really good.
- AstralStorm 8y agoAnd impossible to use thanks to no license information.
- edtruji 8y agoMIT License, it was added after your post https://github.com/conan7882/adversarial-autoencoders-tf/blob/master/LICENSE https://github.com/conan7882/adversarial-autoencoders-tf/blo...
- Lorkki 8y agoThough, 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 https://github.com/conan7882/DeepVision-tensorflow ) That aside, I agree that it's an easier read than most ML code.
- conan7882 8y agoI 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.
- polynomial 8y agoFor 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?
- locuscoeruleus 8y agoFrom 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?
- amelius 8y agoQuestion: what framework is used mostly by academic researchers these days for DL?