Semi-Supervised Adversarial Variational Autoencoder

Ryad Zemouri

doi:10.3390/make2030020

Abstract

We present a method to improve the reconstruction and generation performance of a variational autoencoder (VAE) by injecting an adversarial learning. Instead of comparing the reconstructed with the original data to calculate the reconstruction loss, we use a consistency principle for deep features. The main contributions are threefold. Firstly, our approach perfectly combines the two models, i.e., GAN and VAE, and thus improves the generation and reconstruction performance of the VAE. Secondly, the VAE training is done in two steps, which allows to dissociate the constraints used for the construction of the latent space on the one hand, and those used for the training of the decoder. By using this two-step learning process, our method can be more widely used in applications other than image processing. While training the encoder, the label information is integrated to better structure the latent space in a supervised way. The third contribution is to use the trained encoder for the consistency principle for deep features extracted from the hidden layers. We present experimental results to show that our method gives better performance than the original VAE. The results demonstrate that the adversarial constraints allow the decoder to generate images that are more authentic and realistic than the conventional VAE.

Highlights

Deep generative models (DGMs) are part of the deep models family and are a powerful way to learn any distribution of observed data through unsupervised learning
We present a method to improve the reconstruction and generation performance of a variational autoencoder (VAE) by injecting an adversarial learning
The VAE training is done in two steps, which allows to dissociate the constraints used for the construction of the latent space on the one hand, and those used for the training of the decoder

Summary

Introduction

Deep generative models (DGMs) are part of the deep models family and are a powerful way to learn any distribution of observed data through unsupervised learning. The DGMs are composed mainly by variational autoencoders (VAEs) [1,2,3,4], and generative adversarial networks (GANs) [5]. The VAEs are mainly used to extract features from the input vector in an unsupervised way while the GANs are used to generate synthetic samples through an adversarial learning by achieving an equilibrium between a Generator and a Discriminator. The strength of VAE comes from an extra layer used for sampling the latent vector z and an additional term in the loss function that makes the generation of a more continuous latent space than standard autoencoders. The two major application areas of the VAEs are the biomedical and healthcare recommendation [16,17,18,19], and industrial applications for nonlinear processes monitoring [1,3,4,20,21,22,23,24,25]

Methods

Results

Conclusion