Autoencoding Variational Autoencoder
This work is significant for researchers and practitioners using VAEs, as it identifies and provides a solution for a fundamental inconsistency in VAE inference, leading to more robust learned representations.
This paper addresses the issue that Variational Autoencoders (VAEs) do not consistently encode typical samples generated from their own decoder. By introducing a self-consistency approach, the authors demonstrate that their method leads to representations robust to adversarial attacks, showing improved performance on ColorMnist and CelebA datasets.
Does a Variational AutoEncoder (VAE) consistently encode typical samples generated from its decoder? This paper shows that the perhaps surprising answer to this question is `No'; a (nominally trained) VAE does not necessarily amortize inference for typical samples that it is capable of generating. We study the implications of this behaviour on the learned representations and also the consequences of fixing it by introducing a notion of self consistency. Our approach hinges on an alternative construction of the variational approximation distribution to the true posterior of an extended VAE model with a Markov chain alternating between the encoder and the decoder. The method can be used to train a VAE model from scratch or given an already trained VAE, it can be run as a post processing step in an entirely self supervised way without access to the original training data. Our experimental analysis reveals that encoders trained with our self-consistency approach lead to representations that are robust (insensitive) to perturbations in the input introduced by adversarial attacks. We provide experimental results on the ColorMnist and CelebA benchmark datasets that quantify the properties of the learned representations and compare the approach with a baseline that is specifically trained for the desired property.