论文标题
LED：基于潜在变量密度的估计
LED: Latent Variable-based Estimation of Density
论文作者
论文摘要
现代生成模型大致分为两个主要类别：（1）可以产生高质量随机样品但无法估算新数据点的确切密度的模型，以及（2）提供精确密度估计的模型，以牺牲潜在空间的样本质量和紧凑性为代价。在这项工作中，我们建议LED，这是一种与gan密切相关的新生成模型，不仅允许有效采样，而且允许有效的密度估计。通过最大限度地提高对数的歧视器输出，我们得出了一个替代的对抗优化目标，鼓励生成的数据多样性。这种表述提供了对几种流行生成模型之间关系的见解。此外，我们构建了一个基于流的生成器，该发电机可以计算生成的样品的精确概率，同时允许低维度变量作为输入。我们在各种数据集上的实验结果表明，我们的密度估计器会产生准确的估计值，同时保留了生成的样品质量良好。
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.
