Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RenderGAN: Generating Realistic Labeled Data   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.

37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio