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Generative Adversarial Networks (GANs) are a new network architecture capable of delivering state-of-the-art performance in generating synthetic images in various domains. We train a network called VeGAN (Vehicle Generative Adversarial Network) to generate realistic images of vehicles that look like images taken from a top-down view of an unmanned aerial vehicle (UAV). The generated images are used as additional training data for a semantic segmentation network, which precisely detects vehicles in recordings of traffic on highways. While images are commonly generated randomly for a content-based augmentation, we leverage ideas from the domain of active learning. Using a network which is based on the InfoGAN architecture allows mapping existing vehicle images to a latent space representation. After mapping the complete training dataset, we perform the augmentation in the latent space. The …