  Batch Normalization (BN) is a common technique used to speed-up and stabilize
training. On the other hand, the learnable parameters of BN are commonly used
in conditional Generative Adversarial Networks (cGANs) for representing
class-specific information using conditional Batch Normalization (cBN). In this
paper we propose to generalize both BN and cBN using a Whitening and Coloring
based batch normalization. We show that our conditional Coloring can represent
categorical conditioning information which largely helps the cGAN qualitative
results. Moreover, we show that full-feature whitening is important in a
general GAN scenario in which the training process is known to be highly
unstable. We test our approach on different datasets and using different GAN
networks and training protocols, showing a consistent improvement in all the
tested frameworks. Our CIFAR-10 conditioned results are higher than all
previous works on this dataset.
