基于基完备化理论和嵌入多层感知机的深度网络结构设计

Deepnetwork structure design based on base completion and embedded multi-layer perceptron

为了进一步改善经典卷积神经网络的识别性能,首先证明了跨层传输实质上是基的完备化过程,然后基于多类型特征提取结构、嵌入多层感知机以及跨层传输结构提出一种新型深度卷积网络——期望网络.经分析发现,期望网络中的多类型特征提取结构可提取不同类型的特征,嵌入多层感知机可生成期望图并标定不同类型特征的权重,跨层传输结构可缓解网络性能退化的问题.仿真实验结果表明,在数据集CIFAR-10、数据集CIFAR-100和数据集SVHN上,相比于ResNet网络、深度监督网络和Highw ay网络等经典深度卷积网络,期望网络的误识别率均有不同程度的下降.

In order to improve the recognition performance of classical convolutional neural networks,the cross-layer transmission was proved to be essentially a base completion process,and then a new type of deep convolutional network named expectation network(ExpNet)was proposed based on the multi-type feature extraction structure,the embedded multi-layer perceptron(MLP),and the cross-layer transmission structure.The analysis results show that the multi-type feature extraction structure in the expectation network can extract different types of features.The embedding multi-layer perceptron can generate the expectation graph and calibrate the weights of different types of features.The cross-layer transmission structure can alleviate the problem of network performance degradation.The simulation results show that on the CIFAR-10,the CIFAR-100 and the SVHN datasets,the misrecognition rate in the expectation network is lower than those of the ResNet,the deep supervision,and the Highway networks.