School of Engineering, Sun Yat-sen University, Guangzhou 510006, PR China.
The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, Guangzhou 510080, PR China.
Neural Netw. 2018 Sep;105:340-345. doi: 10.1016/j.neunet.2018.05.015. Epub 2018 Jun 15.
A novel sparsity-based stochastic pooling which integrates the advantages of max-pooling, average-pooling and stochastic pooling is introduced. The proposed pooling is designed to balance the advantages and disadvantages of max-pooling and average-pooling by using the degree of sparsity of activations and a control function to obtain an optimized representative feature value ranging from average value to maximum value of a pooling region. The optimized representative feature value is employed for probability weights assignment of activations in normal distribution. The proposed pooling also adopts weighted random sampling with a reservoir for the sampling process to preserve the advantages of stochastic pooling. This proposed pooling is evaluated on several standard datasets in deep learning framework to compare with various classic pooling methods. Experimental results show that it has good performance on improving recognition accuracy. The influence of changes to the feature parameter on recognition accuracy is also investigated.
引入了一种新颖的基于稀疏性的随机池化方法，它集成了最大池化、平均池化和随机池化的优点。所提出的池化方法旨在通过使用激活的稀疏度和控制函数来平衡最大池化和平均池化的优缺点，从而获得一个优化的代表特征值，该特征值在池化区域的平均值到最大值之间变化。优化后的代表特征值用于对正态分布中的激活进行概率权重分配。所提出的池化方法还采用了带有蓄水池的加权随机采样方法，以保留随机池化的优点。在深度学习框架中，该池化方法在几个标准数据集上进行了评估，并与各种经典池化方法进行了比较。实验结果表明，它在提高识别精度方面具有良好的性能。还研究了特征参数变化对识别精度的影响。
