Machine learningMachine learning
迁移学习
迁移学习是一种机器学习范式，它将从源任务或领域模型训练中获得的知识重用于改进不同但相关的目标任务或领域的学习。当目标任务的标注数据稀缺时，它尤其强大，并且是计算机视觉、自然语言处理及其他领域大多数现代深度学习应用的基础。
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Method map
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来源
- Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI: 10.1109/TKDE.2009.191 ↗
- Bengio, Y. (2012). Deep Learning of Representations for Unsupervised and Transfer Learning. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning, PMLR 27, 17–36. link ↗
如何引用本页
ScholarGate. (2026, June 3). Transfer Learning (Domain Adaptation and Knowledge Transfer). ScholarGate. https://scholargate.app/zh/machine-learning/transfer-learning
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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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联邦主动学习主动学习与自监督学习贝叶斯少样本学习贝叶斯半监督学习贝叶斯迁移学习课程学习数据增强 (Data Augmentation)领域适应域自适应强化学习域自适应 Transformer域自适应变分自编码器EfficientNet集成联邦学习集成少样本学习集成度量学习集成自监督学习集成半监督学习集成迁移学习少样本学习度量学习多任务学习神经风格迁移在线联邦学习在线少样本学习在线学习在线自监督学习在线半监督学习在线迁移学习正则化联邦学习正则化少样本学习正则化在线学习正则化迁移学习鲁棒联邦学习自监督主动学习Self-supervised Federated Learning自监督少样本学习自监督图像分类自监督 K-近邻自监督学习自监督 LightGBM自监督逻辑回归自监督情感分析自监督堆叠集成自监督迁移学习半监督联邦学习半监督少样本学习半监督学习半监督度量学习半监督迁移学习T5（Text-to-Text Transfer Transformer）