Semi-Supervised Adversarial Variational Autoencoder

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....

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Vydané v:Machine learning and knowledge extraction Ročník 2; číslo 3; s. 361 - 378
Hlavný autor: Zemouri, Ryad
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: MDPI 01.09.2020
MDPI AG
Predmet:
adversarial learning
Artificial Intelligence
Computer Science
data generation
deep feature consistent
Machine Learning
variational autoencoder
data generation
adversarial learning
variational autoencoder
deep feature consistent
ISSN:2504-4990, 2504-4990
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Popis
Shrnutí: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.
ISSN:2504-4990
2504-4990
DOI:10.3390/make2030020