Unsupervised learning: Difference between revisions

Revision as of 11:20, 10 May 2023

Revision as of 03:21, 20 May 2023

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[[File:Task-guidance.png|thumb|300px|Tendency for a task to employ supervised vs. unsupervised methods. Task names straddling circle boundaries is intentional. It shows that the classical division of imaginative tasks (left) employing unsupervised methods is blurred in today's learning schemes.]]

Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see [[Venn diagram]]); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups. Furthermore, as progress marches onward some tasks employ both methods, and some tasks swing from one to another. For example, image recognition started off as heavily supervised, but became hybrid by employing unsupervised pre-training, and then moved towards supervision again with the advent of [[Dilution_(neural_networks)|dropout]], [[Rectifier_(neural_networks)|reluReLU]], and [[Learning_rate|adaptive learning rates]].

=== Training ===