方法证据记录
Fine-Tuned Multilayer Perceptron
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Fine-Tuned Multilayer Perceptron (Transfer Learning via MLP Weight Adaptation)
分类方法记录 · ml-model / deep-learning
- Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. · DOI 10.1038/323533a0
- Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27, 3320–3328. · URL
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