  The unsupervised training of GANs and VAEs has enabled them to generate
realistic images mimicking real-world distributions and perform image-based
unsupervised clustering or semi-supervised classification. Combining the power
of these two generative models, we introduce Multi-Adversarial Variational
autoEncoder Networks (MAVENs), a novel network architecture that incorporates
an ensemble of discriminators in a VAE-GAN network, with simultaneous
adversarial learning and variational inference. We apply MAVENs to the
generation of synthetic images and propose a new distribution measure to
quantify the quality of the generated images. Our experimental results using
datasets from the computer vision and medical imaging domains---Street View
House Numbers, CIFAR-10, and Chest X-Ray datasets---demonstrate competitive
performance against state-of-the-art semi-supervised models both in image
generation and classification tasks.
