Interpretability Analysis of Convolutional Neural Networks Based on Image Classification
In order to understand the basis for decision making of convolutional neural network in image classification task,so as to optimize the model and reduce the cost of parameter adjustment,it is necessary to analyze the interpretability of convolutional neural network.For this reason,this paper takes the fruit image classification task as the starting point,uses multiple kinds of activation graphs,and analyzes the reasons for the results given by the model from multiple perspectives.In this paper,ResNet model is used to fine tune and achieve better classification performance.The basic analysis of semantic features,occlusion analysis,CAM based interpretability analysis and LIME interpretability analysis are carried out to provide a certain interpretability for convolutional neural networks.The experimental results show that the decision basis of convolutional neural network is more consistent with the semantic concepts understood by human beings.
万方数据
维普
