Abstract
According to the problem of overfitting in the traditional Convolutional Neural Networks(CNN) with large samples, an improved sparse DropConnect algorithm is put forward. With proposed algorithm, the CNN is improved and optimized. The performance of the network in image recognition is verified. Experiments of weld image recognition and classification indicate that the selective ability of the CNN on the sparse feature is improved. High recognition accuracy and classification accuracy of test sample are achieved. Over-fitting in the traditional network model can be avoided. More important, the feature sparsity and discriminative ability of the CNN are increased.
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