  We present a new autoencoder-type architecture that is trainable in an
unsupervised mode, sustains both generation and inference, and has the quality
of conditional and unconditional samples boosted by adversarial learning.
Unlike previous hybrids of autoencoders and adversarial networks, the
adversarial game in our approach is set up directly between the encoder and the
generator, and no external mappings are trained in the process of learning. The
game objective compares the divergences of each of the real and the generated
data distributions with the prior distribution in the latent space. We show
that direct generator-vs-encoder game leads to a tight coupling of the two
components, resulting in samples and reconstructions of a comparable quality to
some recently-proposed more complex architectures.
