1. A computer-implemented method comprising: performing a first feature transformation on a first plurality of inputs by a first classifier using at least one computer processor, wherein each of the first plurality of inputs comprises at least one of a plurality of images, and wherein the first classifier is configured to associate labels with images; receiving a first plurality of outputs from the first feature transformation using the at least one computer processor, wherein each of the first plurality of outputs comprises at least one of a plurality of labels identified as associated with one of the plurality of images; defining a plurality of clusters of the plurality of labels based at least in part on the first plurality of outputs using the at least one computer processor, wherein each of the plurality of clusters is a subset of the plurality of labels commonly associated with at least one of the plurality of images; assigning a pseudolabel to each of the plurality of clusters using the at least one computer processor; performing a second feature transformation on a first input by a second classifier using the at least one computer processor, wherein the first input comprises a first image, and wherein the second classifier is configured to associate pseudolabels with images; receiving a first output from the second feature transformation using the at least one computer processor, wherein the first output comprises a first pseudolabel identified as associated with the first image; identifying a first cluster based at least in part on the first pseudolabel using the at least one computer processor, wherein the first cluster comprises a first subset of the plurality of labels; selecting one of the labels of the first subset; and storing information regarding the selected one of the labels of the first subset in association with the first image in at least one data store.