道探索器:通过激活道可视化探索类分离性
IEEE transactions on visualization and computer graphics
|March 2, 2026
概括
频道探索器 (ChannelExplorer) 是一种视觉分析工具,通过探索类分离性来帮助理解深度神经网络 (DNN). 它有助于识别错误标记的图像,并了解跨各种模型架构的激活通道.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 数据可视化 数据可视化
背景情况:
- 深度神经网络 (DNN) 在视觉任务中表现出色,但它们的内部运作,特别是类分离能力,人们对其了解甚少.
- 分析激活通道和层贡献对于解释DNN行为至关重要.
研究的目的:
- 介绍ChannelExplorer,这是一个用于分析DNN的交互式视觉分析工具.
- 专注于数据驱动的洞察力,以探索跨模型层的类分离性.
- 支持理解各种模型架构,如CNN,GAN,ResNet和稳定扩散.
主要方法:
- ChannelExplorer提供了数据集层面的概述,逐步钻入单个示例.
- 使用三个协调视图:Scatterplot视图用于类混,Jaccard相似性视图用于激活重叠,以及热图视图用于通道模式.
- 总结了跨模型层的激活,揭示了对类可分化的洞察力.
主要成果:
- 展示了 ChannelExplorer 在生成 ImageNet 类层次结构中的实用性.
- 成功使用该工具在数据集中找到错误标记的图像.
- 启用了特定激活通道对模型性能贡献的识别.
- 在稳定扩散模型中方便定位潜态.
结论:
- ChannelExplorer提供了一种新的方法来理解DNN,通过视觉分析专注于类可分离性.
- 该工具是多功能,支持各种深度学习架构和用例.
- 专家用户评估证实了该工具在为DNN行为提供可操作的见解方面的有效性.
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