An Approach for Noisy, Crowdsourced Datasets Utilizing Ensemble Modeling, 'Human Softmax' Distributions, and Entropic Measures of Uncertainty

Noisy, crowdsourced image datasets prove challenging, even for the best neural networks. Two issues which complicate classification 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 a dataset can help identify images where the ground-truth is questionable and quantify the trustworthiness of those samples. We apply stacked generalization consisting of nearly identical ResNets: one utilizing cross-entropy (CXE) and the other Kullback-Liebler Divergence (KLD). The CXE network uses standard labeling drawn from the crowdsourced consensus. In contrast, the KLD network uses probabilistic labeling for each image derived from the distribution of crowdsourced annotations. We refer to this labeling as the Human Softmax (HSM) distribution. 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%. We also perform an analysis of the Shannon entropy of the various models' output distributions to measure classification uncertainty.