  Deep convolutional neural network has made huge revolution and shown its
superior performance on computer vision tasks such as classification and
segmentation. Recent years, researches devote much effort to scaling down size
of network while maintaining its ability, to adapt to the limited memory on
embedded systems like mobile phone. In this paper, we propose a multi-channel
training procedure which can highly facilitate the performance and robust of
the target network. The proposed procedure contains two sets of networks and
two information pipelines which can work independently hinge on the computation
ability of the embedded platform, while in the mean time, the classification
accuracy is also admirably enhanced.
