官方检测报告样本解读

Ideally, machine learning models should be interpretable. However, deep neural networks act as black boxes, making it difficult to understand their decision-making process.

Recent studies have proposed various visualization techniques such as saliency maps and attention mechanisms. These methods help in debugging models and building trust with users.

We introduce a novel framework that combines symbolic reasoning with deep learning to enhance interpretability. Experimental results show significant improvements over baselines.