标题： 通过激活最大化进行表示理解 显示英文标题

标题： Representation Understanding via Activation Maximization

摘要： 理解深度神经网络（DNN）的内部特征表示是实现模型可解释性的基本步骤。 受神经科学方法的启发，这些方法使用视觉刺激来探测生物神经元，最近的深度学习研究已采用激活最大化（AM）来合成能引发人工神经元强烈响应的输入。 在本工作中，我们提出了一种统一的特征可视化框架，适用于卷积神经网络（CNNs）和视觉变压器（ViTs）。 与以往主要关注CNNs最后一层输出层神经元的研究不同，我们将特征可视化扩展到中间层，从而更深入地了解学习到的特征表示的层次结构。 此外，我们研究了如何利用激活最大化生成对抗样本，揭示DNNs的潜在漏洞和决策边界。 我们的实验表明，我们的方法在传统的CNNs和现代的ViT中都有效，突显了其通用性和解释价值。 显示英文摘要

摘要： Understanding internal feature representations of deep neural networks (DNNs) is a fundamental step toward model interpretability. Inspired by neuroscience methods that probe biological neurons using visual stimuli, recent deep learning studies have employed Activation Maximization (AM) to synthesize inputs that elicit strong responses from artificial neurons. In this work, we propose a unified feature visualization framework applicable to both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Unlike prior efforts that predominantly focus on the last output-layer neurons in CNNs, we extend feature visualization to intermediate layers as well, offering deeper insights into the hierarchical structure of learned feature representations. Furthermore, we investigate how activation maximization can be leveraged to generate adversarial examples, revealing potential vulnerabilities and decision boundaries of DNNs. Our experiments demonstrate the effectiveness of our approach in both traditional CNNs and modern ViT, highlighting its generalizability and interpretive value.