Maximum likelihood estimation of energy-based models is a challenging problem
due to the intractability of the log-likelihood gradient. In this work, we
propose learning both the energy function and an amortized approximate sampling
mechanism using a neural generator network, which provides an efficient
approximation of the log-likelihood gradient. The resulting objective requires
maximizing entropy of the generated samples, which we perform using recently
proposed nonparametric mutual information estimators. Finally, to stabilize the
resulting adversarial game, we use a zero-centered gradient penalty derived as
a necessary condition from the score matching literature. The proposed
technique can generate sharp images with Inception and FID scores competitive
with recent GAN techniques, does not suffer from mode collapse, and is
competitive with state-of-the-art anomaly detection techniques.

使用神经生成网络学习能量函数和一种分摊求近似（amortized approximate）的采样机制，结合最近提出的非参数互信息估计器来最大化生成样本的熵，使用必要条件从分数匹配（score matching）文献中衍生出的零中心梯度惩罚来稳定生成的对抗游戏。该技术可以生成与最新的生成对抗网络技术竞争的清晰图像，不会出现模式崩溃，并且在异常检测方面具有与现有技术相媲美的竞争力。