Max-Pooling Dropout for Regularization of Convolutional Neural Networks

  • Haibing Wu
  • Xiaodong GuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)


Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage.


Deep learning Convolutional neural network Max-pooling dropout 



This work was supported in part by National Natural Science Foundation of China under grant 61371148.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electronic EngineeringFudan UniversityShanghaiChina

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