11. The computer-implemented method of claim 5 , further comprising: providing a set of training inputs to a second machine learning tool associated with the predetermined task using the at least one computer processor, wherein each of the training inputs comprises a training image; receiving a plurality of training outputs from the second machine learning tool, wherein each of the training outputs comprises a label identified as associated with one of the training images; and defining a plurality of clusters of labels based at least in part on the training outputs, wherein at least two of the labels in each of the clusters is identified as associated with one of the training images, and wherein the first cluster is one of the plurality of clusters.
11. The computer-implemented method of claim 5 , further comprising: providing a set of training inputs to a second machine learning tool associated with the predetermined task using the at least one computer processor, wherein each of the training inputs comprises a training image; receiving a plurality of training outputs from the second machine learning tool, wherein each of the training outputs comprises a label identified as associated with one of the training images; and defining a plurality of clusters of labels based at least in part on the training outputs, wherein at least two of the labels in each of the clusters is identified as associated with one of the training images, and wherein the first cluster is one of the plurality of clusters. 13. The computer-implemented method of claim 11 , wherein a first one of the training inputs comprises a first label identified as associated with a first one of the training images to a first degree of confidence, wherein a second one of the training inputs comprises a second label identified as associated with the first one of the training images to a second degree of confidence, and wherein the method further comprises: defining the first cluster including at least the first label and the second label.