Bandingkan metode

	Pembelajaran Multitugas	Pembelajaran Kurikulum	Transfer Learning
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning	Machine learning
Tahun asal	1997	2009	2010 (formalized); 1990s (early roots)
Pencetus	Rich Caruana	Yoshua Bengio et al.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Tipe	Inductive transfer method	Training strategy	Learning paradigm
Sumber perintis	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.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Alias	MTL, Joint Learning, Shared Representation Learning, Çok Görevli Öğrenme	Scheduled Training, Difficulty-Based Training, Self-Paced Learning, Müfredat Öğrenimi	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Terkait	3	3	3
Ringkasan	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.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.