# Adversarial Variational Embedding for Robust Semi-supervised Learning

## TL;DR

This paper introduces a novel adversarial variational embedding framework that combines VAE and GAN to improve semi-supervised learning by producing exclusive latent codes and meaningful data generation.

## Contribution

It proposes AVAE, a new framework that leverages VAE++ and GAN to enhance semi-supervised classification with more exclusive latent representations and better data generation control.

## Key findings

- Outperforms state-of-the-art semi-supervised models on four real-world datasets.
- Produces more exclusive and meaningful latent codes for classification.
- Enhances the quality and control of generated data.

## Abstract

Semi-supervised learning is sought for leveraging the unlabelled data when labelled data is difficult or expensive to acquire. Deep generative models (e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial Networks (GANs) have recently shown promising performance in semi-supervised classification for the excellent discriminative representing ability. However, the latent code learned by the traditional VAE is not exclusive (repeatable) for a specific input sample, which prevents it from excellent classification performance. In particular, the learned latent representation depends on a non-exclusive component which is stochastically sampled from the prior distribution. Moreover, the semi-supervised GAN models generate data from pre-defined distribution (e.g., Gaussian noises) which is independent of the input data distribution and may obstruct the convergence and is difficult to control the distribution of the generated data. To address the aforementioned issues, we propose a novel Adversarial Variational Embedding (AVAE) framework for robust and effective semi-supervised learning to leverage both the advantage of GAN as a high quality generative model and VAE as a posterior distribution learner. The proposed approach first produces an exclusive latent code by the model which we call VAE++, and meanwhile, provides a meaningful prior distribution for the generator of GAN. The proposed approach is evaluated over four different real-world applications and we show that our method outperforms the state-of-the-art models, which confirms that the combination of VAE++ and GAN can provide significant improvements in semisupervised classification.