  Generative models dealing with modeling a~joint data distribution are
generally either autoencoder or GAN based. Both have their pros and cons,
generating blurry images or being unstable in training or prone to mode
collapse phenomenon, respectively. The objective of this paper is to construct
a~model situated between above architectures, one that does not inherit their
main weaknesses. The proposed LCW generator (Latent Cramer-Wold generator)
resembles a classical GAN in transforming Gaussian noise into data space. What
is of utmost importance, instead of a~discriminator, LCW generator uses kernel
distance. No adversarial training is utilized, hence the name generator. It is
trained in two phases. First, an autoencoder based architecture, using kernel
measures, is built to model a manifold of data. We propose a Latent Trick
mapping a Gaussian to latent in order to get the final model. This results in
very competitive FID values.
