  The field of Weakly Supervised Learning (WSL) has recently seen a surge of
popularity, with numerous papers addressing different types of "supervision
deficiencies", namely: poor quality, non adaptability, and insufficient
quantity of labels. Regarding quality, label noise can be of different types,
including completely-at-random, at-random or even not-at-random. All these
kinds of label noise are addressed separately in the literature, leading to
highly specialized approaches. This paper proposes an original, encompassing,
view of Weakly Supervised Learning, which results in the design of generic
approaches capable of dealing with any kind of label noise. For this purpose,
an alternative setting called "Biquality data" is used. It assumes that a small
trusted dataset of correctly labeled examples is available, in addition to an
untrusted dataset of noisy examples. In this paper, we propose a new
reweigthing scheme capable of identifying noncorrupted examples in the
untrusted dataset. This allows one to learn classifiers using both datasets.
Extensive experiments that simulate several types of label noise and that vary
the quality and quantity of untrusted examples, demonstrate that the proposed
approach outperforms baselines and state-of-the-art approaches.
