  The decoder-based machine learning generative algorithms such as Generative
Adversarial Networks (GAN), Variational Auto-Encoders (VAE), Transformers show
impressive results when constructing objects similar to those in a training
ensemble. However, the generation of new objects builds mainly on the
understanding of the hidden structure of the training dataset followed by a
sampling from a multi-dimensional normal variable. In particular each sample is
independent from the others and can repeatedly propose same type of objects. To
cure this drawback we introduce a kernel-based measure quantization method that
can produce new objects from a given target measure by approximating it as a
whole and even staying away from elements already drawn from that distribution.
This ensures a better diversity of the produced objects. The method is tested
on classic machine learning benchmarks.
