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

DOST -- Domain Obedient Self-supervised Training for Multi Label Classification with Noisy Labels