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Implicit Discriminator in Variational Autoencoder
Prateek Munjal, Akanksha Paul,
Published in Institute of Electrical and Electronics Engineers Inc.
Pages: 1 - 8
Recently generative models have focused on combining the advantages of variational autoencoders (VAE) and generative adversarial networks (GAN) for good reconstruction and generative abilities. In this work we introduce a novel hybrid architecture, Implicit Discriminator in Variational Autoencoder (IDVAE), that combines a VAE and a GAN, which does not need an explicit discriminator network. The fundamental premise of the IDVAE architecture is that the encoder of a VAE and the discriminator of a GAN utilize common features and therefore can be trained as a shared network, while the decoder of the VAE and the generator of the GAN can be combined to learn a single network. This results in a simple two-tier architecture that has the properties of both a VAE and a GAN. The qualitative and quantitative experiments on real-world benchmark datasets demonstrate that IDVAE performs better than the state of the art hybrid approaches. We experimentally validate that IDVAE can be easily extended to work in a conditional setting and demonstrate its performance on complex datasets.
About the journal
JournalProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo