机器学习22：可解释机器学习（Explainable Machine Learning）（下）

摘要

        本周深入探讨可解释机器学习中的全局解释方法，核心在于通过分析模型内部参数（如卷积层滤波器）生成代表性图像，以揭示模型整体决策依据。关键技术包括：利用梯度上升生成最大化滤波器响应的图像X*，直观展示特征侦测内容（如数字分类器中的笔画结构）；针对对抗攻击暴露的模型敏感性问题，引入约束函数R(X)提升生成图像可识别性；结合图像生成器G，通过优化潜在向量z获得清晰类别表征。扩展方法LIME以简单模型局部模仿复杂网络行为，为黑箱模型提供可解释性支持，推动模型决策机制的透明化与安全性。

Abstract

        This week delves into global interpretation methods in explainable machine learning, focusing on revealing overall model decision mechanisms by analyzing internal parameters (e.g., convolutional filters) to generate representative images. Key techniques include: using gradient ascent to create images X* that maximize filter responses, visually demonstrating feature detection (e.g., stroke structures in digit classifiers); addressing model sensitivity exposed by adversarial attacks through constraint functions R(X) to enhance image recognizability; and integrating image generator G to optimize latent vectors z for clear category representations. Extension methods like LIME locally mimic complex model behaviors with simpler models, providing interpretability support for black-box models and promoting transparency and security in decis