k*分布:使用局部邻域分析评估深度神经网络的潜空间
IEEE transactions on neural networks and learning systems
|September 16, 2024
概括
我们介绍了k*分布,这是一个可视化神经网络隐藏空间的新方法. 它保留了局部结构,与t-SNE或UMAP不同,揭示了不同的样本分布,以更好地理解网络.
科学领域:
- 机器学习 机器学习
- 数据可视化 数据可视化
- 人工智能的人工智能
背景情况:
- 像t-SNE和UMAP这样的传统的缩小维度技术在神经网络潜伏空间中扭曲了局部邻里结构.
- 这种扭曲阻碍了在特定类别内的样本分布的清晰可视化和分析.
研究的目的:
- 介绍k*分布及其可视化技术,以保护当地社区结构.
- 能够详细分析神经网络如何在它们的潜在空间中处理不同类别.
- 为神经网络隐藏空间提供当代可视化方法的更深入的理解.
主要方法:
- 开发了k*分布和相应的可视化技术.
- 利用当地的社区分析来确保对个别类的样本分布结构的保存.
- 将k*分布进行比较,以分析神经网络中的类处理.
主要成果:
- 在潜空间中确定了三种不同的样本分布:断裂,重叠和集群.
- 证明样本分布在神经网络中的不同类别之间有显著的差异.
- 展示了k*分布分析在各种网络架构,层,输入转换和数据分割 (培训/测试) 中的适用性.
结论:
- k*分布提供了一种通过保留局部结构来可视化神经网络隐藏空间的优质方法.
- 这种技术有助于更深入地理解在学习的表征中的类内和类间的关系.
- k*分布是分析和理解各种神经网络模型内部运作的宝贵工具.
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