Сравнение методов

	Обучение с переносом и обучение с подкреплением	Transfer Learning with Convolutional Neural Network
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2009 (survey); concept from early 2000s	2010–2014
Автор метода	Taylor, M. E. & Stone, P.	Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.
Тип	Transfer learning paradigm for sequential decision-making	Transfer learning applied to convolutional neural networks
Основополагающий источник	Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Другие названия	Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN
Связанные	4	4
Сводка	Transfer Learning with Reinforcement Learning (Transfer RL) is a training paradigm in which knowledge acquired by an agent in one or more source tasks — encoded as policy weights, value functions, or learned representations — is reused to accelerate or improve learning in a related but different target task. It directly addresses the sample-inefficiency that plagues reinforcement learning from scratch in complex or expensive environments.	Transfer Learning with CNN reuses a convolutional neural network that has already been trained on a large dataset — most commonly ImageNet — and adapts its learned feature detectors to a new, often smaller target dataset. This lets researchers achieve strong image-recognition performance without the massive compute and data resources required to train a CNN from scratch.