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。

改进具有深度特征一致性和生成对抗训练的变分自动编码器

Improving Variational Autoencoder with Deep Feature Consistent and  Generative Adversarial Training

Facial attribute analysis has received considerable attention when deep
learning techniques made remarkable breakthroughs in this field over the past
few years. Deep learning based facial attribute analysis consists of two basic
sub-issues: facial attribute estimation (FAE), which recognizes whether facial
attributes are present in given images, and facial attribute manipulation
(FAM), which synthesizes or removes desired facial attributes. In this paper,
we provide a comprehensive survey of deep facial attribute analysis from the
perspectives of both estimation and manipulation. First, we summarize a general
pipeline that deep facial attribute analysis follows, which comprises two
stages: data preprocessing and model construction. Additionally, we introduce
the underlying theories of this two-stage pipeline for both FAE and FAM.
Second, the datasets and performance metrics commonly used in facial attribute
analysis are presented. Third, we create a taxonomy of state-of-the-art methods
and review deep FAE and FAM algorithms in detail. Furthermore, several
additional facial attribute related issues are introduced, as well as relevant
real-world applications. Finally, we discuss possible challenges and promising
future research directions.

本论文从估计和操作两个角度全面调查了深度面部属性分析。介绍了面部属性估计和操作的数据预处理和模型构建通用流程，并对常用的数据集和性能指标进行了介绍。此外，对最新的面部属性分析方法进行了分类和细节方面的审查，并提出了相关的面部属性应用问题和现实世界的应用。最后，讨论了可能的挑战和未来的研究方向。