  A conditional Generative Adversarial Network allows for generating samples
conditioned on certain external information. Being able to recover latent and
conditional vectors from a condi- tional GAN can be potentially valuable in
various applications, ranging from image manipulation for entertaining purposes
to diagnosis of the neural networks for security purposes. In this work, we
show that it is possible to recover both latent and conditional vectors from
generated images given the generator of a conditional generative adversarial
network. Such a recovery is not trivial due to the often multi-layered
non-linearity of deep neural networks. Furthermore, the effect of such recovery
applied on real natural images are investigated. We discovered that there
exists a gap between the recovery performance on generated and real images,
which we believe comes from the difference between generated data distribution
and real data distribution. Experiments are conducted to evaluate the recovered
conditional vectors and the reconstructed images from these recovered vectors
quantitatively and qualitatively, showing promising results.
