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RenderGAN: Generating Realistic Labeled Data

Tim Landgraf, Leon Sixt, Benjamin Wild – 2016

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 to 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.

Titel
RenderGAN: Generating Realistic Labeled Data
Verfasser
Tim Landgraf, Leon Sixt, Benjamin Wild
Verlag
arXiv preprint arXiv:1611.01331
Datum
2016-11-04
Quelle/n
Art
Text
Größe oder Länge
15 pages