Modern generative models are roughly divided into two main categories: (1) models that can produce high-quality random samples, but cannot estimate the exact density of new data points and (2) those that provide exact density estimation, at the expense of sample quality and compactness of the latent space. In this work we propose LED, a new generative model closely related to GANs, that allows not only efficient sampling but also efficient density estimation. By maximizing log-likelihood on the output of the discriminator, we arrive at an alternative adversarial optimization objective that encourages generated data diversity. This formulation provides insights into the relationships between several popular generative models. Additionally, we construct a flow-based generator that can compute exact probabilities for generated samples, while allowing low-dimensional latent variables as input. Our experimental results, on various datasets, show that our density estimator produces accurate estimates, while retaining good quality in the generated samples.

翻译：现代基因模型大致分为两大类:(1) 能够产生高质量随机样本的模型,但无法估计新数据点的确切密度,以及(2) 提供精确密度估计的模型,以降低样本质量和潜在空间的紧凑性为代价。在这项工作中,我们提出了LED,这是一个与GANs密切相关的新的基因模型,不仅允许高效取样,而且允许高效的密度估计。通过最大限度地扩大对歧视者产出的日志相似性,我们达成了另一个鼓励生成数据多样性的对抗性优化目标。这一配方为几种流行的基因模型之间的关系提供了洞察力。此外,我们建造了一个流基生成的生成器,可以计算生成样品的精确概率,同时允许低维潜在变量作为输入。我们在各种数据集上的实验结果显示,我们的密度估计器生成了准确的估计数,同时保持了生成样本的高质量。