Data_Sheet_1_RenderGAN: Generating Realistic Labeled Data.pdf

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.

深度卷积神经网络（DCNNs）在众多计算机视觉任务上展现出卓越的性能。由于其庞大的参数空间，在监督学习环境下进行训练时，需要大量的标记样本。手动标注数据的成本使得DCNNs的应用变得不可行。我们提出了一种名为RenderGAN的创新框架，该框架通过结合3D模型与生成对抗网络（GAN）框架，能够生成大量逼真的、标记清晰的图像。在我们的方法中，图像增强（如光照、背景和细节）是从未标记的数据中学习的，以确保生成的图像不仅具有极高的逼真度，同时保留了从3D模型中已知的标签。我们将RenderGAN框架应用于生成附着在蜜蜂上的条形码标记图像。在RenderGAN生成的数据上训练DCNN，其性能显著优于在多种基线数据上的训练。