Title: Bidirectional Conditional Generative Adversarial Networks,
Abstract: Conditional Generative Adversarial Networks (cGANs) are generative models
that can produce data samples ($x$) conditioned on both latent variables ($z$)
and known auxiliary information ($c$). We propose the Bidirectional cGAN
(BiCoGAN), which effectively disentangles $z$ and $c$ in the generation process
and provides an encoder that learns inverse mappings from $x$ to both $z$ and
$c$, trained jointly with the generator and the discriminator. We present
crucial techniques for training BiCoGANs, which involve an extrinsic factor
loss along with an associated dynamically-tuned importance weight. As compared
to other encoder-based cGANs, BiCoGANs encode $c$ more accurately, and utilize
$z$ and $c$ more effectively and in a more disentangled way to generate
samples.