Scenic：一种场景描述和场景生成语言
Scenic: A Language for Scenario Specification and Scene Generation
摘要：
我们提出了一种新的概率程序设计语言的设计和分析的感知系统，特别是那些基于机器学习。具体来说，我们认为训练感知系统处理罕见事件，测试其性能在不同的条件下，和调试失败的问题。我们展示了概率程序设计语言如何通过指定对有趣的输入类型进行编码的分布并对这些输入进行采样以生成专门的训练集和测试集来帮助解决这些问题。更一般地说，这种语言可以用于网络物理系统和机器人来编写环境模型，这是任何形式分析的必要先决条件。在本文中，我们专注于自动驾驶汽车和机器人等系统，其环境是一个“场景”，一个物理对象和代理的配置。我们设计了一个特定领域的语言，风景，描述“场景”，分布在场景。作为一种概率程序设计语言，Scenic允许将分布分配给场景的特征，以及声明性地对场景施加硬约束和软约束。我们开发专门的技术，从产生的分布采样，利用风景区的特定于域的语法提供的结构。最后，我们将Scenic应用于一个卷积神经网络的案例研究中，该网络旨在检测道路图像中的汽车，从而提高其性能，超越了最先进的合成数据生成方法。
英文摘要：
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs and sampling these to generate specialized training and test sets. More generally, such languages can be used for cyber-physical systems and robotics to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems like autonomous cars and robots, whose environment is a "scene", a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing "scenarios" that are distributions over scenes. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in a case study on a convolutional neural network designed to detect cars in road images, improving its performance beyond that achieved by state-of-the-art synthetic data generation methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
