18. The non-transitory machine-readable medium of claim 16 , wherein the operations further comprise: using the recognition engine to determine weak instances in the set of examples; in accordance with a determination that a weak instance is in the set of examples: modifying the set of examples by: removing the weak instance from the set of examples; and adding to the set of examples a replacement image selected from the set of images, the replacement image not being associated with the label associated with the clustered images; and training the recognition engine using the clustered images as positive examples and the modified set of examples as negative examples.
16. A non-transitory machine-readable medium having instructions embodied thereon, the instructions executable by one or more processors of a system to perform operations comprising: accessing a set of images uploaded to a network-based system by a plurality of users of a network-based service provided by the network-based system, each image of the set of images having a text description of the corresponding image as provided by a user of the plurality of users who uploaded the corresponding image; generating a set of associated weak labels for each image of the set of images based on the corresponding text description of the respective image, each set of associated weak labels including one or more weak labels for the respective image that are derived from the corresponding text description of the respective image; generating a weakly-labeled dataset comprising the set of images; clustering images of the set of images based on each of the clustered images sharing a particular label of the weak labels; accessing a set of examples selected from the set of images uploaded to the network-based system, each example of the set of examples being selected based on the respective example not being associated with the particular label associated with the clustered images; training a recognition engine using the clustered images as positive examples and the set of examples as negative examples; and generating strong labels for the clustered images using the trained recognition engine, the strong labels including the particular label based on a ranking of the particular label with respect to the cluster as provided by the trained recognition engine.