Regularized Transfer Learning
Regularized Transfer Learning applies explicit penalty terms to a transfer learning pipeline to control how much a model shifts away from source-domain knowledge when adapting to a new target domain. The regularizer discourages negative transfer — the harmful carry-over of irrelevant source patterns — while preserving beneficial shared representations and preventing overfitting when target-domain labels are scarce.
Kilderegistrering
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- 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
- Li, Z., Nie, F., Chang, X., & Yang, Y. (2014). Beyond trace norm: Robust matrix recovery via bi-sparsity pursuit. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1736–1742. · URL
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