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.

翻译：自编码器能够以无监督方式学习有用的数据表示，并已广泛应用于各种机器学习和计算机视觉任务。本文提出了将可逆神经网络（INN）训练为（变分）自编码器的方法，我们将其称为INN（变分）自编码器。我们在MNIST、CIFAR和CelebA上的实验表明，当瓶颈尺寸较小时，我们的INN自编码器能达到与经典自编码器相似的结果。然而，当瓶颈尺寸较大时，我们的INN自编码器性能优于经典自编码器。基于实验结果，我们假设INN自编码器可能不存在内在信息损失，因此其网络深度不受限于最大层数——超过该层数后只能获得次优结果。