arXiv cs.AI· Lorenzo Cirillo, Irene Amerini, Andrea Cavallaro·· 10 小时前AI 评分31
深度神经网络机制可解释性综述：方法、框架与展望
Mechanistic Interpretability of Deep Neural Networks: Methods, Frameworks, and Perspectives
AI 导读
一篇综述梳理了深度神经网络机制可解释性的研究进展，聚焦于通过识别激活对特定行为具有因果责任的组件来揭示网络内部机制。文章覆盖图像、音频、语言等信号域，讨论内部激活作为跨层变换的结构化信号、单个神经元常表征多个概念等难点，并总结面向因果推断分析的方法与框架。
正文 · 原文
Abstract:Deep neural networks underpin fundamental tasks in a wide range of signal domains, such as image, audio, and language processing. However, their internal mechanisms are notably difficult to interpret. Internal activations are structured signals transformed across layers that may encode a multitude of interdependent relationships, as individual neurons frequently represent multiple underlying concepts. Mechanistic interpretability aims to uncover the internal mechanisms of neural networks by identifying components whose activations are causally responsible for specific behaviors. In such a context, this article provides an overview of advances in mechanistic interpretability for the analysis of causal inference in deep neural networks.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2611.13868 [cs.LG] |
| (or arXiv:2611.13868v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13868 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lorenzo Cirillo [view email]
[v1]
Tue, 10 Nov 2026 10:29:14 UTC (4,659 KB)
来源：arXiv cs.AI · arxiv.org