Understanding neural network through neuron level visualization

Neurons are the fundamental units of neural networks. In this paper, we propose a method for explaining neural networks by visualizing the learning process of neurons. For a trained neural network, the proposed method obtains the features learned by each neuron and displays the features in a human-understandable form. The features learned by different neurons are combined to analyze the working mechanism of different neural network models. The method is applicable to neural networks without requiring any changes to the architectures of the models. In this study, we apply the proposed method to both Fully Connected Networks (FCNs) and Convolutional Neural Networks (CNNs) trained using the backpropagation learning algorithm. We conduct experiments on models for image classification tasks to demonstrate the effectiveness of the method. Through these experiments, we gain insights into the working mechanisms of various neural network architectures and evaluate neural network interpretability from diverse perspectives.

Automated summary

Neurons are fundamental units of neural networks, and this paper proposes a method for explaining neural networks by visualizing the learning process of neurons. The method can analyze the working mechanism of different neural network models without requiring any changes to the architectures. The effectiveness of the method is demonstrated through experiments on various neural network architectures for image classification tasks, providing insights into the interpretability of neural networks from diverse perspectives.