标题： 自回归模型：有何用途？ 显示英文标题

标题： Autoregressive Models: What Are They Good For?

摘要： 自回归（AR）模型已成为无监督学习的一种流行工具，实现了最先进的对数似然估计值。 我们研究了AR模型作为两种设置下的密度估计器的使用情况——作为图像翻译的学习信号和作为异常值检测器——并发现这些密度估计值远不如以前认为的可靠。 我们从经验与理论的角度检查了潜在的优化问题，并提供了一个示例来说明该问题。 大量研究表明，密度估计值与感知质量不相关，且对下游任务没有帮助。 显示英文摘要

摘要： Autoregressive (AR) models have become a popular tool for unsupervised learning, achieving state-of-the-art log likelihood estimates. We investigate the use of AR models as density estimators in two settings -- as a learning signal for image translation, and as an outlier detector -- and find that these density estimates are much less reliable than previously thought. We examine the underlying optimization issues from both an empirical and theoretical perspective, and provide a toy example that illustrates the problem. Overwhelmingly, we find that density estimates do not correlate with perceptual quality and are unhelpful for downstream tasks.