Machine learning
Dropout
Dropout 是一种用于训练深度神经网络的随机正则化技术，由 Srivastava、Hinton、Krizhevsky、Sutskever 和 Salakhutdinov 于 2014 年提出。在每次训练步骤中，每个神经元以概率 (1 − p) 被独立关闭，从而防止网络中的单元过度协同适应，并因此减少过拟合。
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来源
- Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929–1958. link ↗
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 7: Regularization for Deep Learning). MIT Press. ISBN: 978-0-262-03561-3
如何引用本页
ScholarGate. (2026, June 3). Dropout Regularization for Deep Neural Networks. ScholarGate. https://scholargate.app/zh/deep-learning/dropout
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