  Generative models with an encoding component such as autoencoders currently
receive great interest. However, training of autoencoders is typically
complicated by the need to train a separate encoder and decoder model that have
to be enforced to be reciprocal to each other. To overcome this problem,
by-design reversible neural networks (RevNets) had been previously used as
generative models either directly optimizing the likelihood of the data under
the model or using an adversarial approach on the generated data. Here, we
instead investigate their performance using an adversary on the latent space in
the adversarial autoencoder framework. We investigate the generative
performance of RevNets on the CelebA dataset, showing that generative RevNets
can generate coherent faces with similar quality as Variational Autoencoders.
This first attempt to use RevNets inside the adversarial autoencoder framework
slightly underperformed relative to recent advanced generative models using an
autoencoder component on CelebA, but this gap may diminish with further
optimization of the training setup of generative RevNets. In addition to the
experiments on CelebA, we show a proof-of-principle experiment on the MNIST
dataset suggesting that adversary-free trained RevNets can discover meaningful
latent dimensions without pre-specifying the number of dimensions of the latent
sampling distribution. In summary, this study shows that RevNets can be
employed in different generative training settings.
  Source code for this study is at
https://github.com/robintibor/generative-reversible
