방법 비교

	능동 학습	다중 작업 학습 (Multitask Learning, MTL)
분야	머신러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2009	1997
창시자	Burr Settles	Rich Caruana
유형	Interactive supervised learning framework	Inductive transfer method
원전	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.	Caruana, R. (1997). Multitask learning. Machine Learning, 28(1), 41–75.
별칭	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	MTL, Joint Learning, Shared Representation Learning, Çok Görevli Öğrenme
관련	2	3
요약	Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires.	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.