# Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training

## TL;DR

This paper introduces a novel training approach for variational autoencoders that combines deep feature consistency with adversarial training, resulting in more realistic image generation and improved facial attribute manipulation.

## Contribution

The paper proposes a combined deep feature consistent and adversarial training method for VAEs, enhancing image realism and embedding quality for facial attribute tasks.

## Key findings

- Generated face images with clearer features and natural textures.
- Achieved state-of-the-art performance in facial attribute prediction.
- Learned powerful embeddings for facial attribute manipulation.

## Abstract

We present a new method for improving the performances of variational autoencoder (VAE). In addition to enforcing the deep feature consistent principle thus ensuring the VAE output and its corresponding input images to have similar deep features, we also implement a generative adversarial training mechanism to force the VAE to output realistic and natural images. We present experimental results to show that the VAE trained with our new method outperforms state of the art in generating face images with much clearer and more natural noses, eyes, teeth, hair textures as well as reasonable backgrounds. We also show that our method can learn powerful embeddings of input face images, which can be used to achieve facial attribute manipulation. Moreover we propose a multi-view feature extraction strategy to extract effective image representations, which can be used to achieve state of the art performance in facial attribute prediction.