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Improving variational autoencoder with deep feature consistent and generative adversarial training

Hou, Xianxu; Sun, Ke; Shen, Linlin; Qiu, Guoping


Xianxu Hou

Ke Sun

Linlin Shen

Professor of Visual Information Processing


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.


Hou, X., Sun, K., Shen, L., & Qiu, G. (2019). Improving variational autoencoder with deep feature consistent and generative adversarial training. Neurocomputing, 341, 183-194.

Journal Article Type Article
Acceptance Date Mar 9, 2019
Online Publication Date Mar 13, 2019
Publication Date May 14, 2019
Deposit Date Mar 13, 2019
Publicly Available Date Mar 14, 2020
Journal Neurocomputing
Print ISSN 0925-2312
Electronic ISSN 1872-8286
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 341
Pages 183-194
Keywords Image Generation; Facial Attributes; Generative model; VAE; GAN
Public URL
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