  Deep Learning systems have proven to be extremely successful for image
recognition tasks for which significant amounts of training data is available,
e.g., on the famous ImageNet dataset. We demonstrate that for robotics
applications with cheap camera equipment, the low image quality,
however,influences the classification accuracy, and freely available databases
cannot be exploited in a straight forward way to train classifiers to be used
on a robot. As a solution we propose to train a network on degrading the
quality images in order to mimic specific low quality imaging systems.
Numerical experiments demonstrate that classification networks trained by using
images produced by our quality degrading network along with the high quality
images outperform classification networks trained only on high quality data
when used on a real robot system, while being significantly easier to use than
competing zero-shot domain adaptation techniques.
