  This paper proposes two important contributions for conditional Generative
Adversarial Networks (cGANs) to improve the wide variety of applications that
exploit this architecture. The first main contribution is an analysis of cGANs
to show that they are not explicitly conditional. In particular, it will be
shown that the discriminator and subsequently the cGAN does not automatically
learn the conditionality between inputs. The second contribution is a new
method, called a contrario cGAN, that explicitly models conditionality for both
parts of the adversarial architecture via a novel a contrario loss that
involves training the discriminator to learn unconditional (adverse) examples.
This leads to a novel type of data augmentation approach for GANs (a contrario
learning) which allows to restrict the search space of the generator to
conditional outputs using adverse examples. Extensive experimentation is
carried out to evaluate the conditionality of the discriminator by proposing a
probability distribution analysis. Comparisons with the cGAN architecture for
different applications show significant improvements in performance on well
known datasets including, semantic image synthesis, image segmentation,
monocular depth prediction and "single label"-to-image using different metrics
including Fr\'echet Inception Distance (FID), mean Intersection over Union
(mIoU), Root Mean Square Error log (RMSE log) and Number of
statistically-Different Bins (NDB).
