5. A computer-implemented method comprising: providing a first input to a first machine learning tool associated with a predetermined task using at least one computer processor, wherein the first input comprises a first image; performing a first feature transformation on the first input by the first machine learning tool using the at least one computer processor; receiving a first output from the first machine learning tool using the at least one computer processor, wherein the first output comprises first information regarding the first image; identifying a first cluster of labels based at least in part on the first output; selecting one of the labels of the first cluster based at least in part on the first information regarding the first image; and storing the selected one of the labels of the first cluster in association with the first image in at least one data store.
7. The computer-implemented method of claim 5 , wherein selecting the one of the labels of the first cluster further comprises: providing a second input to the first machine learning tool associated with the predetermined task using the at least one computer processor, wherein the second input comprises the first image and at least some of the first information regarding the first image; performing a second feature transformation on the second input by the first machine learning tool using the at least one computer processor; receiving a second output from the first machine learning tool using the at least one computer processor, wherein the second output comprises second information regarding the first image; identifying a second cluster of labels based at least in part on the second output, wherein the second cluster is a subset of the first cluster; and selecting the one of the labels of the first cluster based at least in part on the second information regarding the first image, wherein the selected one of the labels of the first cluster is one of the labels of the second cluster.