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Semi-Supervised Adversarial Variational Autoencoder

Cedric-Lab, CNAM, HESAM Université, 292 rue Saint Martin, CEDEX 03, 750141 Paris, France
Mach. Learn. Knowl. Extr. 2020, 2(3), 361-378;
Received: 1 August 2020 / Revised: 1 September 2020 / Accepted: 3 September 2020 / Published: 6 September 2020
We present a method to improve the reconstruction and generation performance of a variational autoencoder (VAE) by injecting an adversarial learning. Instead of comparing the reconstructed with the original data to calculate the reconstruction loss, we use a consistency principle for deep features. The main contributions are threefold. Firstly, our approach perfectly combines the two models, i.e., GAN and VAE, and thus improves the generation and reconstruction performance of the VAE. Secondly, the VAE training is done in two steps, which allows to dissociate the constraints used for the construction of the latent space on the one hand, and those used for the training of the decoder. By using this two-step learning process, our method can be more widely used in applications other than image processing. While training the encoder, the label information is integrated to better structure the latent space in a supervised way. The third contribution is to use the trained encoder for the consistency principle for deep features extracted from the hidden layers. We present experimental results to show that our method gives better performance than the original VAE. The results demonstrate that the adversarial constraints allow the decoder to generate images that are more authentic and realistic than the conventional VAE. View Full-Text
Keywords: variational autoencoder; adversarial learning; deep feature consistent; data generation variational autoencoder; adversarial learning; deep feature consistent; data generation
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MDPI and ACS Style

Zemouri, R. Semi-Supervised Adversarial Variational Autoencoder. Mach. Learn. Knowl. Extr. 2020, 2, 361-378.

AMA Style

Zemouri R. Semi-Supervised Adversarial Variational Autoencoder. Machine Learning and Knowledge Extraction. 2020; 2(3):361-378.

Chicago/Turabian Style

Zemouri, Ryad. 2020. "Semi-Supervised Adversarial Variational Autoencoder" Machine Learning and Knowledge Extraction 2, no. 3: 361-378.

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