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What Is Supervised Learning?
The problem with supervised learning is that it just doesn't scale. If I want to annotate more concepts, if I want to have this various types of fine-grained concepts then it won't really scale. So now you come up to these sort of different learning paradigms. For example, semi-supervised learning where the idea is of course you have this annotated corpus of supervised data and you have lots of unlabeled images. The idea is that the algorithm should basically try to measure some kind of consistency or really try toMeasure some kind of signal on this sort of unlabeling data in order to make it self more confident about what it's really trying to predict