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Energy-based models (EBMs) are versatile density estimation models thatdirectly parameterize an unnormalized log density. Although very flexible, EBMslack a specified normalization constant of the model, making the likelihood ofthe model computationally intractable. Several approximate samplers andvariational inference techniques have been proposed to estimate the likelihoodgradients for training. These techniques have shown promising results ingenerating samples, but little attention has been paid to the statisticalaccuracy of the estimated density, such as determining the relative importanceof different classes in a dataset. In this work, we propose a new maximumlikelihood training algorithm for EBMs that uses a different type of generativemodel, normalizing flows (NF), which have recently been proposed to facilitatesampling. Our method fits an NF to an EBM during training so that anNF-assisted sampling scheme provides an accurate gradient for the EBMs at alltimes, ultimately leading to a fast sampler for generating new data.

Balanced Training of Energy-Based Models with Adaptive Flow Sampling