方法对比
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| 半监督多层感知机× | 微调多层感知机× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2006–2013 | 1986 (MLP); fine-tuning practice formalised c. 2014 |
| 提出者≠ | Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H. | Rumelhart, Hinton & Williams (MLP); Yosinski et al. (fine-tuning analysis) |
| 类型≠ | Semi-supervised feedforward neural network | Supervised deep learning with pre-trained weight initialisation |
| 开创性文献≠ | Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 | Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI ↗ |
| 别名 | SSL-MLP, semi-supervised MLP, semi-supervised feedforward network, partially supervised multilayer perceptron | fine-tuned MLP, adapted MLP, domain-adapted multilayer perceptron, MLP fine-tuning |
| 相关 | 4 | 4 |
| 摘要≠ | A semi-supervised multilayer perceptron (SSL-MLP) is a feedforward neural network trained on a small pool of labeled examples together with a larger pool of unlabeled examples. By combining supervised cross-entropy loss on labeled data with an unsupervised consistency or pseudo-label objective on unlabeled data, it extracts far more signal from the data than a purely supervised MLP trained on labels alone. | 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. |
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