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| 정규화된 소수샷 학습× | 정규화된 전이 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2016-2020 | 2000s–2010s |
| 창시자≠ | Multiple (Chen et al., Tian et al., Snell et al., and others) | Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors |
| 유형≠ | Meta-learning framework with explicit regularization | Regularized supervised/semi-supervised learning framework |
| 원전≠ | Chen, W., Liu, Y., Kira, Z., Wang, Y. F., & Huang, J. (2019). A Closer Look at Few-Shot Classification. International Conference on Learning Representations (ICLR). 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 | FSL with regularization, regularized meta-learning, few-shot learning with regularization, regularized episodic learning | regularized domain adaptation, transfer learning with regularization, penalized transfer learning, regularized fine-tuning |
| 관련≠ | 5 | 6 |
| 요약≠ | Regularized few-shot learning augments standard few-shot learning pipelines with explicit regularization mechanisms — such as weight decay, dropout, data augmentation, label smoothing, or manifold constraints — to reduce overfitting to the tiny support sets that define each episode. This produces more generalizable models when only one to thirty labeled examples per class are available. | 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. |
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