Deep convolutional neural networks (CNNs) have proven to be successful for learning task-specific features that achieve state-of-the-art performance on many computer vision tasks. For object detection applications, the introduction of region-based CNNs (R-CNNs), and its successors, Fast R-CNN and Faster R-CNN, has produced relatively high accuracies and run-time efficient results. With Faster R-CNN, a region proposal network (RPN) is employed to share convolutional layers for both object proposals and detection with no loss in accuracy. However, these approaches are trained in a fully supervised manner, where a large number of samples for individual object classes are required, and classes are pre-determined by manual annotation. Large-scale supervision leads to limitations in utility for many real-world applications, including those involving difficult-to-detect, small, and sparse target objects in variable environments. Alternatively, exemplar learning is a paradigm for discovering visual similarities in an unsupervised fashion from potentially very small numbers of examples. Surrogate classes or outliers are discovered via the inherent empirical characteristics of the objects themselves. In this work, we merge the strengths of CNN structures with pre-processing steps borrowed from exemplar learning. We employ a semi-supervised approach that combines the ability to use generically-learned class-relatedness with CNN-based detectors. We train and test the approach on a set of aerial imagery generated from unmanned aircraft systems (UAS) for challenging real-world, small object detection tasks.