  The scarcity of data annotated at the desired level of granularity is a
recurring issue in many applications. Significant amounts of effort have been
devoted to developing weakly supervised methods tailored to each individual
setting, which are often carefully designed to take advantage of the particular
properties of weak supervision regimes, form of available data and prior
knowledge of the task at hand. Unfortunately, it is difficult to adapt these
methods to new tasks and/or forms of data, which often require different weak
supervision regimes or models. We present a general-purpose method that can
solve any weakly supervised learning problem irrespective of the weak
supervision regime or the model. The proposed method turns any off-the-shelf
strongly supervised classifier into a weakly supervised classifier and allows
the user to specify any arbitrary weakly supervision regime via a loss
function. We apply the method to several different weak supervision regimes and
demonstrate competitive results compared to methods specifically engineered for
those settings.
