Resumen de Algorithm of overfitting avoidance in CNN based on maximum pooled and weight decay

Guanghan Li, Xiangcheng Jian, Zhicheng Wen, Jamal AlSultan

• This paper aims to eradicate the poor performance of the convolutional neural network (CNN) for intelligent analysisand detection in samples. Moreover, to avoid overfitting of the CNN model during the training process, an algorithm isproposed for the fusion of maximum pooled and weight decay. Firstly, the maximum pooled method for the pooling layeris explored after mask processing to reduce the number of irrelevant neurons. Secondly, when updating the neuron weightparameters, the weight decay is introduced to further cut down complexity in model training. The experimental comparisonshows that the overfitting avoidance algorithm can reduce the detection error rate by more than 10% in image detectionthan other methods, and it has better generalisation