Sammenlign metoder

	Multitask Learning	Curriculum Learning
Fagfelt	Dyp læring	Dyp læring
Familie	Machine learning	Machine learning
Opprinnelsesår	1997	2009
Opphavsperson	Rich Caruana	Yoshua Bengio et al.
Type	Inductive transfer method	Training strategy
Opprinnelig kilde	Caruana, R. (1997). Multitask learning. Machine Learning, 28(1), 41–75.	Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. International Conference on Machine Learning (ICML), 41–48.
Alias	MTL, Joint Learning, Shared Representation Learning, Çok Görevli Öğrenme	Scheduled Training, Difficulty-Based Training, Self-Paced Learning, Müfredat Öğrenimi
Relaterte	3	3
Sammendrag	Multitask Learning (MTL) is a machine learning paradigm in which a model is trained simultaneously on multiple related tasks, sharing representations across them to improve generalization. Introduced formally by Rich Caruana in 1997, MTL draws on the intuition that auxiliary tasks act as inductive bias, providing extra supervision signals that help the shared layers learn richer, more robust feature representations than single-task training would yield.	Curriculum Learning is a training strategy for machine learning models, introduced by Bengio et al. in 2009, in which training examples are presented in a meaningful order—typically from easy to hard—rather than at random. Inspired by how humans and animals learn progressively, it organizes training data into a curriculum that starts with simpler, cleaner, or more representative samples and gradually introduces harder or more complex examples as the model matures.