  Ensemble methods are generally regarded to be better than a single model if
the base learners are deemed to be "accurate" and "diverse." Here we
investigate a semi-supervised ensemble learning strategy to produce
generalizable blind image quality assessment models. We train a multi-head
convolutional network for quality prediction by maximizing the accuracy of the
ensemble (as well as the base learners) on labeled data, and the disagreement
(i.e., diversity) among them on unlabeled data, both implemented by the
fidelity loss. We conduct extensive experiments to demonstrate the advantages
of employing unlabeled data for BIQA, especially in model generalization and
failure identification.
