Symmetric Wasserstein Autoencoders

Abstract

Leveraging the framework of Optimal Transport, we introduce a new family of generative autoencoders with a learnable prior, called Symmetric Wasserstein Autoencoders (SWAEs). We propose to symmetrically match the joint distributions of the observed data and the latent representation induced by the encoder and the decoder. The resulting algorithm jointly optimizes the modelling losses in both the data and the latent spaces with the loss in the data space leading to the denoising effect. With the symmetric treatment of the data and the latent representation, the algorithm implicitly preserves the local structure of the data in the latent space. To further improve the quality of the latent representation, we incorporate a reconstruction loss into the objective, which significantly benefits both the generation and reconstruction. We empirically show the superior performance of SWAEs over the state-of-the-art generative autoencoders in terms of classification, reconstruction, and generation.

arXiv commentAccepted by UAI2021

References in corpus (14)

• Auto-Encoding Variational Bayes
• Sequence to Sequence Learning with Neural Networks
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Pixel Recurrent Neural Networks
• NICE: Non-linear Independent Components Estimation
• Variational Inference with Normalizing Flows
• Density estimation using Real NVP
• NVAE: A Deep Hierarchical Variational Autoencoder
• Structured Disentangled Representations
• RNADE: The real-valued neural autoregressive density-estimator
• On the Convergence and Robustness of Training GANs with Regularized Optimal Transport
• VAE Learning via Stein Variational Gradient Descent
• The Deep Weight Prior
• MIM: Mutual Information Machine