Abstract:
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 demonstrates that IDVAE perform 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.
