  Autoencoders are able to learn useful data representations in an unsupervised
matter and have been widely used in various machine learning and computer
vision tasks. In this work, we present methods to train Invertible Neural
Networks (INNs) as (variational) autoencoders which we call INN (variational)
autoencoders. Our experiments on MNIST, CIFAR and CelebA show that for low
bottleneck sizes our INN autoencoder achieves results similar to the classical
autoencoder. However, for large bottleneck sizes our INN autoencoder
outperforms its classical counterpart. Based on the empirical results, we
hypothesize that INN autoencoders might not have any intrinsic information loss
and thereby are not bounded to a maximal number of layers (depth) after which
only suboptimal results can be achieved.
