  In this work we present a method for fine-tuning pre-trained GANs with
features from different datasets, resulting in the transformation of the output
distribution into a new distribution with novel characteristics. The weights of
the generator are updated using the weighted sum of the losses from a
cross-dataset classifier and the frozen weights of the pre-trained
discriminator. We discuss details of the technical implementation and share
some of the visual results from this training process.
