Drifting models
This novel method approaches the generation task as an iterative process during training time. The generation per se is single-step.
Consider a neural network that receives as input data with dimension C and produces a response with dimension D.
The C-dimensional input can be considered a noise sample. The idea is that the model can learn to produce a meaningful D-dimensional pushforward distribution that approximates a data distribution.