Method for Training Convolution Neural Network With Multiple Classifiers

To improve the classification accuracy of convolution neural network similar to conditional deep learning network (CDLN), a method of joint training with multiple classifiers was proposed in this paper. When training the network, all the error signals of the classifiers were applied to update weights by Back Propagation. In the experiments, CDLN-L and CDLN-A based on LeNet-5 and AlexNet were studied on the MINIST, CIFAR-100 and Pascal Voc databases, and an increase of 4.39% in classification accuracy was achieved. The experiments demonstrate that the proposed method can improve the accuracy of the network similar to CDLN.