  This article introduces a multiple classifier method to improve the
performance of concatenate-designed neural networks, such as ResNet and
DenseNet, with the purpose to alleviate the pressure on the final classifier.
We give the design of the classifiers, which collects the features produced
between the network sets, and present the constituent layers and the activation
function for the classifiers, to calculate the classification score of each
classifier. We use the L2 normalization method to obtain the classifier score
instead of the Softmax normalization. We also determine the conditions that can
enhance convergence. As a result, the proposed classifiers are able to improve
the accuracy in the experimental cases significantly, and show that the method
not only has better performance than the original models, but also produces
faster convergence. Moreover, our classifiers are general and can be applied to
all classification related concatenate-designed network models.
