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| Fine-Tuned Multilayer Perceptron× | 파인튜닝된 LSTM× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1986 (MLP); fine-tuning practice formalised c. 2014 | 2018 (fine-tuning paradigm formalised); LSTM core: 1997 |
| 창시자≠ | Rumelhart, Hinton & Williams (MLP); Yosinski et al. (fine-tuning analysis) | Howard, J. & Ruder, S. (ULMFiT); foundational LSTM by Hochreiter & Schmidhuber |
| 유형≠ | Supervised deep learning with pre-trained weight initialisation | Supervised sequential model with transfer learning |
| 원전≠ | Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI ↗ | Howard, J., & Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 328–339. DOI ↗ |
| 별칭 | fine-tuned MLP, adapted MLP, domain-adapted multilayer perceptron, MLP fine-tuning | Fine-Tuned LSTM, LSTM Fine-Tuning, Pre-trained LSTM with Task Adaptation, LSTM Transfer Learning |
| 관련≠ | 4 | 6 |
| 요약≠ | A Fine-Tuned Multilayer Perceptron starts from weights learned on a source task — or a large general-purpose dataset — and continues training on a smaller target dataset with a reduced learning rate. This reuse of pre-learned representations allows the MLP to converge faster and generalise better than training from scratch, especially when labelled target data is scarce. | Fine-Tuned LSTM adapts a Long Short-Term Memory network pre-trained on a large corpus to a specific downstream task — such as text classification, sentiment analysis, or sequence labeling — by continuing training on task-specific labeled data. Popularised by the ULMFiT framework, this approach achieves strong performance even when labeled data is scarce. |
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