Dilution (neural networks)
Appearance
Dropout is a technique of reducing overfitting in neural networks by preventing complex co-adaptations on training data. It is a very efficient way of performing model averaging with neural networks.[1] The term "dropout" refers to dropping out units (both hidden and visible) in a neural network.[2]
See also
References
- ^ "(1207.0580) Improving neural networks by preventing co-adaptation of feature detectors". Arxiv.org. Retrieved July 26, 2015.
- ^ "Dropout: A Simple Way to Prevent Neural Networks from Overfitting". Jmlr.org. Retrieved July 26, 2015.