  Performing classification on noisy, crowdsourced image datasets can prove
challenging even for the best neural networks. Two issues which complicate the
problem on such datasets are class imbalance and ground-truth uncertainty in
labeling. The AL-ALL and AL-PUB datasets - consisting of tightly cropped,
individual characters from images of ancient Greek papyri - are strongly
affected by both issues. The application of ensemble modeling to such datasets
can help identify images where the ground-truth is questionable and quantify
the trustworthiness of those samples. As such, we apply stacked generalization
consisting of nearly identical ResNets with different loss functions: one
utilizing sparse cross-entropy (CXE) and the other Kullback-Liebler Divergence
(KLD). Both networks use labels drawn from a crowd-sourced consensus. This
consensus is derived from a Normalized Distribution of Annotations (NDA) based
on all annotations for a given character in the dataset. For the second
network, the KLD is calculated with respect to the NDA. For our ensemble model,
we apply a k-nearest neighbors model to the outputs of the CXE and KLD
networks. Individually, the ResNet models have approximately 93% accuracy,
while the ensemble model achieves an accuracy of > 95%, increasing the
classification trustworthiness. We also perform an analysis of the Shannon
entropy of the various models' output distributions to measure classification
uncertainty. Our results suggest that entropy is useful for predicting model
misclassifications.
