论文标题
全光：完全有条件的光芒以获得更真实的图像生成
Full-Glow: Fully conditional Glow for more realistic image generation
论文作者
论文摘要
自主代理，例如无人驾驶汽车，需要大量标记的视觉数据进行培训。获取此类数据的一种可行方法是训练具有收集的真实数据的生成模型，然后使用模型中的合成图像来增强收集的真实数据集，并通过控制场景布局和地面真相标记生成。在本文中，我们提出了全光，这是一种完全有条件的基于发光的架构，用于生成新颖街头场景的合理且逼真的图像，鉴于语义分割图表示场景布局。基准比较表明，从验证的PSPNET的语义分割性能方面，我们的模型与最新作品均优于最新作品。这表明，与其他模型相比，我们模型的图像与相同类型的场景和对象的真实图像相似，使其适合于视觉语义分割或对象识别系统的训练数据。
Autonomous agents, such as driverless cars, require large amounts of labeled visual data for their training. A viable approach for acquiring such data is training a generative model with collected real data, and then augmenting the collected real dataset with synthetic images from the model, generated with control of the scene layout and ground truth labeling. In this paper we propose Full-Glow, a fully conditional Glow-based architecture for generating plausible and realistic images of novel street scenes given a semantic segmentation map indicating the scene layout. Benchmark comparisons show our model to outperform recent works in terms of the semantic segmentation performance of a pretrained PSPNet. This indicates that images from our model are, to a higher degree than from other models, similar to real images of the same kinds of scenes and objects, making them suitable as training data for a visual semantic segmentation or object recognition system.
