  Multi-label classification (MLC) is a generalization of standard
classification where multiple labels may be assigned to a given sample. In the
real world, it is more common to deal with noisy datasets than clean datasets,
given how modern datasets are labeled by a large group of annotators on
crowdsourcing platforms, but little attention has been given to evaluating
multi-label classifiers with noisy labels. Exploiting label correlations now
becomes a standard component of a multi-label classifier to achieve competitive
performance. However, this component makes the classifier more prone to poor
generalization - it overfits labels as well as label dependencies. We identify
three common real-world label noise scenarios and show how previous approaches
per-form poorly with noisy labels. To address this issue, we present a
Context-Based Multi-LabelClassifier (CbMLC) that effectively handles noisy
labels when learning label dependencies, without requiring additional
supervision. We compare CbMLC against other domain-specific state-of-the-art
models on a variety of datasets, under both the clean and the noisy settings.
We show CbMLC yields substantial improvements over the previous methods in most
cases.
