Using PSPNet and UNet to analyze the internal parameter relationship and visualization of the convolutional neural network
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Convolutional neural network(CNN) has achieved great success in many fields, but due to the huge number of parameters, it is very difficult to study. Then, can we start from the parameters themselves to explore the relationship between the internal parameters of CNN? This paper proposes to use the convolution layer parameters substitution with the same convolution kernel setting to explore the relationship between the internal parameters of CNN and proposes to use the CNN visualization method to check the relationship. Using the visualization method, the forward propagation process of CNN is visualized. It is an intuitive representation of how CNN learns. According to the experiments, this paper believes that 1. Residual layer parameters of ResNet are correlated, and some layers can be substituted for each other; 2. Image segmentation is a process of first learning image texture features and then locating and segmentation.
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