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| 자기 지도 전이 학습× | 퓨샷 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2018–2020 (modern consolidation) | 2011–2017 |
| 창시자≠ | LeCun, Y. (concept); Devlin et al. (BERT, NLP); Chen et al. (SimCLR, vision) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| 유형≠ | Learning paradigm (self-supervised pre-training + fine-tuning) | Meta-learning / low-data learning paradigm |
| 원전≠ | Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 1597–1607. link ↗ | Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗ |
| 별칭 | self-supervised pre-training, SSL-based transfer learning, representation transfer from self-supervised models, contrastive pre-training with transfer | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| 관련≠ | 6 | 4 |
| 요약≠ | Self-supervised transfer learning combines two powerful paradigms: a model first learns rich representations from unlabeled data using self-supervised pretext tasks, then those learned representations are transferred and fine-tuned on a downstream task with limited labeled data. This approach underlies landmark systems such as BERT in NLP and SimCLR and DINO in computer vision, dramatically reducing labeled-data requirements across many domains. | Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited. |
| ScholarGate데이터셋 ↗ |
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