  The maximum likelihood principle advocates parameter estimation via
optimization of the data likelihood function. Models estimated in this way can
exhibit a variety of generalization characteristics dictated by, e.g.
architecture, parameterization, and optimization bias. This work addresses
model learning in a setting where there further exists side-information in the
form of an oracle that can label samples as being outside the support of the
true data generating distribution. Specifically we develop a new denoising
diffusion probabilistic modeling (DDPM) methodology, Gen-neG, that leverages
this additional side-information. Our approach builds on generative adversarial
networks (GANs) and discriminator guidance in diffusion models to guide the
generation process towards the positive support region indicated by the oracle.
We empirically establish the utility of Gen-neG in applications including
collision avoidance in self-driving simulators and safety-guarded human motion
generation.
