  Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable
performance on many computer vision tasks. Due to their large parameter space,
they require many labeled samples when trained in a supervised setting. The
costs of annotating data manually can render the use of DCNNs infeasible. We
present a novel framework called RenderGAN that can generate large amounts of
realistic, labeled images by combining a 3D model and the Generative
Adversarial Network framework. In our approach, image augmentations (e.g.
lighting, background, and detail) are learned from unlabeled data such that the
generated images are strikingly realistic while preserving the labels known
from the 3D model. We apply the RenderGAN framework to generate images of
barcode-like markers that are attached to honeybees. Training a DCNN on data
generated by the RenderGAN yields considerably better performance than training
it on various baselines.

Neural and Evolutionary Computing;Computer Vision and Pattern Recognition