  The enormous demand for annotated data brought forth by deep learning
techniques has been accompanied by the problem of annotation noise. Although
this issue has been widely discussed in machine learning literature, it has
been relatively unexplored in the context of "multi-label classification" (MLC)
tasks which feature more complicated kinds of noise. Additionally, when the
domain in question has certain logical constraints, noisy annotations often
exacerbate their violations, making such a system unacceptable to an expert.
This paper studies the effect of label noise on domain rule violation incidents
in the MLC task, and incorporates domain rules into our learning algorithm to
mitigate the effect of noise. We propose the Domain Obedient Self-supervised
Training (DOST) paradigm which not only makes deep learning models more aligned
to domain rules, but also improves learning performance in key metrics and
minimizes the effect of annotation noise. This novel approach uses domain
guidance to detect offending annotations and deter rule-violating predictions
in a self-supervised manner, thus making it more "data efficient" and domain
compliant. Empirical studies, performed over two large scale multi-label
classification datasets, demonstrate that our method results in improvement
across the board, and often entirely counteracts the effect of noise.
