潜伏Zoom:在生成潜伏空间中无扩展,用于在深度神经网络中视觉探索本地性能
IEEE transactions on visualization and computer graphics
|January 14, 2026
概括
本研究引入了一种用于使用生成数据评估深度神经网络 (DNN) 的新方法. 它可视化了DNN在生成模型中的性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 传统的深度神经网络 (DNN) 评估依赖于事先收集的真实世界数据,限制了全面的绩效评估.
- 现有的方法往往无法在各种特征组合和变异中探索DNN行为.
研究的目的:
- 提出一种新的方法来评估DNN,通过在生成的数据上探索它们的本地性能.
- 通过利用生成模型的潜在空间来提高DNN测试的全面性.
- 在这个生成的数据空间中开发一个可视化和分析DNN性能的系统.
主要方法:
- 在真实世界的数据上训练生成模型 (GM),以创建生成隐性空间 (GLS).
- 将高维的GLS投影到2D平面上,以可视化DNN性能.
- 开发一个具有多个视图的交互式系统,用于探索DNN行为和诊断性能模式.
主要成果:
- 可视化平面有效地识别了GLS中的感兴趣区域,用于性能分析.
- 开发的系统可以跨越特征细分度对DNN行为进行跨规模的探索.
- 实验结果证实了该方法在DNN评估中的可用性和有效性.
结论:
- 与传统技术相比,这种方法为DNN评估提供了更全面,更有洞察力的方法.
- 在生成的数据中可视化DNN性能,在生成的潜空间中显示出对模型行为的关键见解.
- 拟议的系统促进了详细的性能分析和诊断,以改善DNN开发.
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