Full-Glow: Fully conditional Glow for more realistic image generation

Understand

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.