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.

本文提出了一种改进变分自编码器性能的新方法，通过实施生成对抗训练机制，使得 VAE 输出逼真自然的图像；同时，还使用多视图特征提取策略提取有效的图像表示，从而获得了在面部属性预测上最先进性能。