Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Многослойный перцептрон со слабым контролем× | Дообученная многослойная перцептронная сеть× | |
|---|---|---|
| Область | Глубокое обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2016–2018 | 1986 (MLP); fine-tuning practice formalised c. 2014 |
| Автор метода≠ | Multiple contributors; paradigm formalized by Zhou (2018) and Ratner et al. (2016) | Rumelhart, Hinton & Williams (MLP); Yosinski et al. (fine-tuning analysis) |
| Тип≠ | Feedforward neural network trained under weak supervision | Supervised deep learning with pre-trained weight initialisation |
| Основополагающий источник≠ | Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53. DOI ↗ | Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI ↗ |
| Другие названия | WS-MLP, weakly supervised feedforward network, noisy-label MLP, weak-label multilayer perceptron | fine-tuned MLP, adapted MLP, domain-adapted multilayer perceptron, MLP fine-tuning |
| Связанные≠ | 5 | 4 |
| Сводка≠ | A Weakly Supervised Multilayer Perceptron trains a standard feedforward neural network when only imperfect supervision is available — labels may be noisy, incomplete, crowd-sourced, rule-generated, or derived from distant supervision — enabling learning at scale without the cost of full expert annotation. | 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. |
| ScholarGateНабор данных ↗ |
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