Energy-based models (EBMs) are versatile density estimation models that directly parameterize an unnormalized log density. Although very flexible, EBMs lack a specified normalization constant of the model, making the likelihood of the model computationally intractable. Several approximate samplers and variational inference techniques have been proposed to estimate the likelihood gradients for training. These techniques have shown promising results in generating samples, but little attention has been paid to the statistical accuracy of the estimated density, such as determining the relative importance of different classes in a dataset. In this work, we propose a new maximum likelihood training algorithm for EBMs that uses a different type of generative model, normalizing flows (NF), which have recently been proposed to facilitate sampling. Our method fits an NF to an EBM during training so that an NF-assisted sampling scheme provides an accurate gradient for the EBMs at all times, ultimately leading to a fast sampler for generating new data.

翻译：能量模型（EBMs）是一种通用的密度估计模型，直接参数化未归一化的对数密度。尽管非常灵活，但EBM缺乏指定的归一化常数，使得模型似然计算不可行。已有研究提出了多种近似采样器和变分推断技术来估计似然梯度以进行训练。这些技术在生成样本方面展现了有前景的结果，但很少关注估计密度的统计准确性，例如确定数据集中不同类别的相对重要性。本文提出一种新的EBM最大似然训练算法，该算法利用另一种生成模型——标准化流（NF）——来辅助采样。我们的方法在训练过程中将NF拟合到EBM，使得NF辅助采样方案能始终为EBM提供精确梯度，最终实现快速生成新数据的采样器。