Momentum Contrastive Autoencoder: Using Contrastive Learning for Latent Space Distribution Matching in WAE

Devansh Arpit, Aadyot Bhatnagar, Huan Wang, Caiming Xiong

Keywords: Wasserstein autoencoder, contrastive learning

Abstract:

Wasserstein autoencoder (WAE) shows that matching two distributions is equivalent to minimizing a simple autoencoder (AE) loss under the constraint that the latent space of this AE matches a pre-specified prior distribution. This latent space distribution matching is a core component of WAE, and a challenging task. In this paper, we propose to use the contrastive learning framework that has been shown to be effective for self-supervised representation learning, as a means to resolve this problem. We do so by exploiting the fact that contrastive learning objectives optimize the latent space distribution to be uniform over the unit hyper-sphere, which can be easily sampled from.
We show that using the contrastive learning framework to optimize the WAE loss achieves faster convergence and more stable optimization compared with existing popular algorithms for WAE. This is also reflected in the FID scores on CelebA and CIFAR-10 datasets, and the realistic generated image quality on the CelebA-HQ dataset.

One-sentence Summary: We show that WAE objective can be achieved by applying contrastive learning objective on the latent space of an auto encoder, and this algorithm achieves faster convergence and more stable optimization compared with existing algorithms for WAE.