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| 강화학습에서의 전이 학습× | 컨볼루션 신경망을 이용한 전이 학습× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2009 (survey); concept from early 2000s | 2010–2014 |
| 창시자≠ | Taylor, M. E. & Stone, P. | Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al. |
| 유형≠ | Transfer learning paradigm for sequential decision-making | Transfer learning applied to convolutional neural networks |
| 원전≠ | Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 별칭 | Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL | TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN |
| 관련 | 4 | 4 |
| 요약≠ | 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. |
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