  Complicated and deep neural network models can achieve high accuracy for
image recognition. However, they require a huge amount of computations and
model parameters, which are not suitable for mobile and embedded devices.
Therefore, MobileNet was proposed, which can reduce the number of parameters
and computational cost dramatically. The main idea of MobileNet is to use a
depthwise separable convolution. Two hyper-parameters, a width multiplier and a
resolution multiplier are used to the trade-off between the accuracy and the
latency. In this paper, we propose a new architecture to improve the MobileNet.
Instead of using the resolution multiplier, we use a depth multiplier and
combine with either Fractional Max Pooling or the max pooling. Experimental
results on CIFAR database show that the proposed architecture can reduce the
amount of computational cost and increase the accuracy simultaneously.
