  Scarcity of training data is one of the prominent problems for deep networks
which require large amounts data. Data augmentation is a widely used method to
increase the number of training samples and their variations. In this paper, we
focus on improving vehicle detection performance in aerial images and propose a
generative augmentation method which does not need any extra supervision than
the bounding box annotations of the vehicle objects in the training dataset.
The proposed method increases the performance of vehicle detection by allowing
detectors to be trained with higher number of instances, especially when there
are limited number of training instances. The proposed method is generic in the
sense that it can be integrated with different generators. The experiments show
that the method increases the Average Precision by up to 25.2% and 25.7% when
integrated with Pluralistic and DeepFill respectively.
