  Complex image processing and computer vision systems often consist of a
processing pipeline of functional modules. We intend to replace parts or all of
a target pipeline with deep neural networks to achieve benefits such as
increased accuracy or reduced computational requirement. To acquire a large
amount of labeled data necessary to train the deep neural network, we propose a
workflow that leverages the target pipeline to create a significantly larger
labeled training set automatically, without prior domain knowledge of the
target pipeline. We show experimentally that despite the noise introduced by
automated labeling and only using a very small initially labeled data set, the
trained deep neural networks can achieve similar or even better performance
than the components they replace, while in some cases also reducing
computational requirements.
