  The variational autoencoder is a well defined deep generative model that
utilizes an encoder-decoder framework where an encoding neural network outputs
a non-deterministic code for reconstructing an input. The encoder achieves this
by sampling from a distribution for every input, instead of outputting a
deterministic code per input. The great advantage of this process is that it
allows the use of the network as a generative model for sampling from the data
distribution beyond provided samples for training. We show in this work that
utilizing batch normalization as a source for non-determinism suffices to turn
deterministic autoencoders into generative models on par with variational ones,
so long as we add a suitable entropic regularization to the training objective.
