  We propose Generative Well-intentioned Networks (GWINs), a novel framework
for increasing the accuracy of certainty-based, closed-world classifiers. A
conditional generative network recovers the distribution of observations that
the classifier labels correctly with high certainty. We introduce a reject
option to the classifier during inference, allowing the classifier to reject an
observation instance rather than predict an uncertain label. These rejected
observations are translated by the generative network to high-certainty
representations, which are then relabeled by the classifier. This architecture
allows for any certainty-based classifier or rejection function and is not
limited to multilayer perceptrons. The capability of this framework is assessed
using benchmark classification datasets and shows that GWINs significantly
improve the accuracy of uncertain observations.
