In a paper published on the preprint server Arxiv.org, Besides addressing the task of visual classification, StarNet supports the task of weakly supervised few-shot object detection, such that only a small amount of noisy data is required to achieve reasonable accuracy with it. StarNet could increase transparency in and reduce the amount of training data needed for new visual domains, like self-driving cars and autonomous industrial robots. By extension, it could cut down on deployment time for AI projects involving classifiers, which surveys show ranges between 8 and 90 days. StarNet consists of a few-shot classifier module attached to an extractor, both of which are trained in a meta-learning fashion where episodes are randomly sampled from classes.