Network Interpretation

Network Interpretation is a technique for analyzing the internal mechanisms of deep neural networks, aimed at enhancing the transparency and interpretability of models. By examining the network architecture and weights, this technology can uncover key features and logical relationships in the model's decision-making process, thereby increasing understanding and trust in the model’s behavior. In the field of computer vision, Network Interpretation helps to optimize model performance, improve algorithm robustness and generalization capabilities, and provides crucial support for model debugging and security assessment.