摘 要
随着人工智能技术的快速发展，深度学习模型在众多领域取得了显著成果，但其“黑箱”特性限制了实际应用中的信任度和可靠性。为解决这一问题，本文聚焦于深度学习模型的可解释性研究，旨在通过构建透明且易于理解的模型机制，提升其在复杂任务中的可信度。研究从理论与实践两方面展开，首先分析了现有可解释性方法的局限性，并提出一种基于注意力机制与特征交互的新型框架，该框架能够有效捕捉模型决策过程中的关键因素。其次，结合梯度传播与局部近似技术，设计了一种多尺度解释算法，用于揭示模型内部的逻辑关系。实验结果表明，所提方法不仅能够在图像分类、自然语言处理等任务中提供清晰的解释依据，还能显著提高模型预测的准确性与鲁棒性。
关键词：深度学习可解释性 注意力机制 特征交互
Abstract
With the rapid development of artificial intelligence technology, deep learning models have achieved remarkable results in many fields, but its "black box" feature limits the trust and reliability in practical applications. To address this problem, this paper focuses on the interpretability research of deep learning models, aiming to improve their credibility in complex tasks by building transparent and easy to understand model mechanisms. The research is developed from both theory and practice, first analyzing the limitations of existing interpretability methods, and proposing a new fr amework based on the interaction between attention mechanism and features, which can effectively capture the key factors in the model decision process. Second, combining gradient propagation and local approximation techniques, a multi-scale interpretation algorithm is designed to reveal logical relationships within the model. The experimental results show that the proposed method can not only provide clear interpretation basis in image classification and natural language processing, but also significantly improve the accuracy and robustness of model prediction.
Keyword:Depth Learning Explainability Attention Mechanism Feature Interaction
目 录
引言 1
1深度学习模型可解释性概述 1
1.1可解释性的定义与意义 1
1.2深度学习模型的特点 1
1.3可解释性研究的挑战 2
1.4当前研究的主要方法 2
2可解释性评估方法研究 3
2.1评估指标的设计原则 3
2.2定量评估方法分析 3
2.3定性评估方法探讨 4
2.4不同评估方法的比较 4
3可解释性技术实现路径 4
3.1模型内部结构解析技术 4
3.2可视化工具的应用研究 5
3.3局部解释方法的开发 5
3.4全局解释方法的探索 6
4可解释性在实际场景中的应用 6
4.1医疗领域中的应用案例 6
4.2金融风控中的实践分析 7
4.3自然语言处理中的应用 7
4.4计算机视觉中的具体实现 8
结论 8
参考文献 10
致谢 11
