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项目作者： vBLFTePebWNi6c

项目描述 ：
Adversarial Autoencoder and Adv. Variational Bayes simple examples on Keras
高级语言： Jupyter Notebook
项目地址: git://github.com/vBLFTePebWNi6c/example_adversarial-AE-VAE.git


Adversarial Autoencoders & Adversarial Variational Bayes code playground on Keras

In this repo I managed several experimnets to empirically proove the paper’s statements and deeply understand the concept.

Adversarial Autoencoders

Authors propose peculiar way to shape distribution of intermediate layer in autoencoders with Adversarial learning technique. Given this, if there is any distribution P from which we can sample, then we can establish two player game in which encoder should produce new distribution P’ similar to P as much as possible and discriminative network should distinguish from which distribution come arbitrary sample.

3 types of generative models has been proposed:

  • Deterministic AAE — just AE + Adversarial component.
  • Gaussian Prior AAE — VAE-like architecture, but KL divergence replaced with Adversarial component.
  • Universal approximator posterior — compromiss between AE and VAE: there is no explicit parameters of distribution, but noise injection still persists.

Adversarial Variational Bayes

Authors impose strong theoretical background about connection between Adversarial component and VAE, prooves that Adversarial Variational Bayes “yields an exact maximum-likelihood assignment for the parameters of the generative model, as well as the exact posterior distribution over the latent variables given an observation”.

But during my experiments I found that this framework barely capable handle it’s pormises in practice. More opinions on Reddit

Also, during experiments with regular VAE I discovered an interesting behavior: KL divergence keeps small which means that latent code useles and completely mimic noise from standard normal distribution and in same time decoder have ability to restore samples in good quality. More about this fenomena here.