Adversarial training of variational auto-encoders for high fidelity image generation

Salman H. Khan, Munawar Hayat, Nick Barnes

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

7 Citations (Scopus)


Variational auto-encoders (VAEs) provide an attractive solution to image generation problem. However, they tend to produce blurred and over-smoothed images due to their dependence on pixel-wise reconstruction loss. This paper introduces a new approach to alleviate this problem in the VAE based generative models. Our model simultaneously learns to match the data, reconstruction loss and the latent distributions of real and fake images to improve the quality of generated samples. To compute the loss distributions, we introduce an auto-encoder based discriminator model which allows an adversarial learning procedure. The discriminator in our model also provides perceptual guidance to the VAE by matching the learned similarity metric of the real and fake samples in the latent space. To stabilize the overall training process, our model uses an error feedback approach to maintain the equilibrium between competing networks in the model. Our experiments show that the generated samples from our proposed model exhibit a diverse set of attributes and facial expressions and scale up to highresolution images very well.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
EditorsKristin Dana, Tal Hasner, Xiaoming Liu, Rahul Sukthankar
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages9
ISBN (Electronic)9781538648865
ISBN (Print)9781538648872
Publication statusPublished - 2018
Externally publishedYes
EventIEEE Winter Conference on Applications of Computer Vision 2018 - Lake Tahoe, United States of America
Duration: 12 Mar 201815 Mar 2018 (Proceedings)


ConferenceIEEE Winter Conference on Applications of Computer Vision 2018
Abbreviated titleWACV 2018
CountryUnited States of America
CityLake Tahoe
Internet address

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