联营
卷积神经网络
计算机科学
水准点(测量)
人工智能
模式识别(心理学)
特征(语言学)
不变(物理)
上下文图像分类
机器学习
图像(数学)
数学
地图学
哲学
语言学
地理
数学物理
作者
Dingjun Yu,Hanli Wang,Peiqiu Chen,Zhihua Wei
标识
DOI:10.1007/978-3-319-11740-9_34
摘要
Convolutional Neural Network (CNN) is a biologically inspired trainable architecture that can learn invariant features for a number of applications. In general, CNNs consist of alternating convolutional layers, non-linearity layers and feature pooling layers. In this work, a novel feature pooling method, named as mixed pooling, is proposed to regularize CNNs, which replaces the deterministic pooling operations with a stochastic procedure by randomly using the conventional max pooling and average pooling methods. The advantage of the proposed mixed pooling method lies in its wonderful ability to address the over-fitting problem encountered by CNN generation. Experimental results on three benchmark image classification datasets demonstrate that the proposed mixed pooling method is superior to max pooling, average pooling and some other state-of-the-art works known in the literature.
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