Synthetic data has proved increasingly useful in both training and testing machine learning models such as neural networks. The major problem in synthetic data generation is producing meaningful data that is not simply random but reflects properties of real-world data or covers particular cases of interest. In this paper, we show how a probabilistic programming language can be used to guide data synthesis by encoding domain knowledge about what data is useful. Specifically, we focus on data sets arising from "scenes", configurations of physical objects; for example, images of cars on a road. We design a domain-specific language, Scenic, for describing "scenarios" that are distributions over scenes. The syntax of Scenic makes it easy to specify complex relationships between the positions and orientations of objects. 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. A Scenic scenario thereby implicitly defines a distribution over scenes, and we formulate the problem of sampling from this distribution as "scene improvisation". We implement an improviser for Scenic scenarios and apply it in a case study generating synthetic data sets for a convolutional neural network designed to detect cars in road images. Our experiments demonstrate the usefulness of our approach by using Scenic to analyze and improve the performance of the network in various scenarios.

本文提出了一种新的概率编程语言，用于设计和分析感知系统，特别是基于机器学习的系统，在处理罕见事件、测试性能和调试故障方面能够发挥作用；我们将其称为Scenic，并将其应用于自动驾驶汽车和机器人等系统中，以描述场景。

Scenic: 一种情景描述和场景生成语言