  State-of-the-art, high capacity deep neural networks not only require large
amounts of labelled training data, they are also highly susceptible to label
errors in this data, typically resulting in large efforts and costs and
therefore limiting the applicability of deep learning. To alleviate this issue,
we propose a novel meta training and labelling scheme that is able to use
inexpensive unlabelled data by taking advantage of the generalization power of
deep neural networks. We show experimentally that by solely relying on one
network architecture and our proposed scheme of iterative training and
prediction steps, both label quality and resulting model accuracy can be
improved significantly. Our method achieves state-of-the-art results, while
being architecture agnostic and therefore broadly applicable. Compared to other
methods dealing with erroneous labels, our approach does neither require
another network to be trained, nor does it necessarily need an additional,
highly accurate reference label set. Instead of removing samples from a
labelled set, our technique uses additional sensor data without the need for
manual labelling. Furthermore, our approach can be used for semi-supervised
learning.
