Deep network in network

The different CNN models use many layers that typically include a stack of linear convolution layers combined with pooling and normalization layers to extract the characteristics of the images. Unlike these models, and instead of using a linear filter for convolution, the network in network (NiN) model uses a multilayer perception (MLP), a nonlinear function, to replace the linear filter. This article presents a new deep network in network (DNIN) model based on the NiN structure, NiN drag a universal approximator, (MLP) with rectified linear unit (ReLU) to improve classification performance. The use of MLP leads to an increase in the density of the connection. This makes learning more difficult and time learning slower. In this article, instead of ReLU, we use the linear exponential unit (eLU) to solve the vanishing gradient problem that can occur when using ReLU and to speed up the learning process. In addition, a reduction in the convolution filters size by increasing the depth is used in order to reduce the number of parameters. Finally, a batch normalization layer is applied to reduce the saturation of the eLUs and the dropout layer is applied to avoid overfitting. The experimental results on the CIFAR-10 database show that the DNIN can reduce the complexity of implementation due to the reduction in the adjustable parameters. Also the reduction in the filters size shows an improvement in the recognition accuracy of the model.