Abstract
I will describe Generalized Energy Based Models (GEBM) for generative modelling. These models combine two trained components: a base distribution (generally an implicit model, as in a Generative Adversarial Network), which can learn the support of data with low intrinsic dimension in a high dimensional space; and an energy function, to refine the probability mass on the learned support. Both the energy function and base jointly constitute the final model, unlike GANs, which retain only the base distribution (the "generator"). Furthermore, unlike classical energy-based models, the GEBM energy is defined even when the support of the model and data do not overlap. Samples from the trained model can be obtained via Langevin diffusion-based methods (MALA, UAL, HMC). Empirically, the GEBM samples on image-generation tasks are of better quality than those from the learned generator alone, indicating that all else being equal, the GEBM will outperform a GAN of the same complexity.