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）是一种灵活的密度估计模型，可直接参数化未归一化的对数密度。尽管十分灵活，但EBMs缺乏明确的归一化常数，导致模型似然函数在计算上难以处理。已有研究提出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。这些技术在样本生成方面展现出令人鼓舞的结果，但很少关注估计密度的统计精度，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EBMs最大似然训练算法，该算法利用另一种生成模型——归一化流（NF）——来辅助采样（NF近期被提出用于加速采样过程）。我们的方法在训练过程中将NF拟合到EBM，使得NF辅助的采样方案能够始终为EBM提供精确的梯度梯度，最终生成快速的新数据采样器。