Most examinations of neural networks' learned latent spaces typically employ
dimensionality reduction techniques such as t-SNE or UMAP. While these methods
effectively capture the overall sample distribution in the entire learned
latent space, they tend to distort the structure of sample distributions within
specific classes in the subset of the latent space. This distortion complicates
the task of easily distinguishing classes identifiable by neural networks. In
response to this challenge, we introduce the k* Distribution methodology. This
approach focuses on capturing the characteristics and structure of sample
distributions for individual classes within the subset of the learned latent
space using local neighborhood analysis. The key concept is to facilitate easy
comparison of different k* distributions, enabling analysis of how various
classes are processed by the same neural network. This provides a more profound
understanding of existing contemporary visualizations. Our study reveals three
distinct distributions of samples within the learned latent space subset: a)
Fractured, b) Overlapped, and c) Clustered. We note and demonstrate that the
distribution of samples within the network's learned latent space significantly
varies depending on the class. Furthermore, we illustrate that our analysis can
be applied to explore the latent space of diverse neural network architectures,
various layers within neural networks, transformations applied to input
samples, and the distribution of training and testing data for neural networks.
We anticipate that our approach will facilitate more targeted investigations
into neural networks by collectively examining the distribution of different
samples within the learned latent space.

在这篇论文中，作者介绍了一种名为 k * 分布的方法，用于捕获神经网络的学习潜在空间中不同类别的样本分布特征和结构，以便更深入地理解现有的可视化结果。研究揭示了学习潜在空间子集中样本分布的三种不同情况：a) 断裂型，b) 重叠型，c) 聚类型。同时，作者还说明了该分析方法可应用于不同神经网络架构、神经网络中的不同层、输入样本的变换以及神经网络的训练和测试数据分布的探索。这一方法有望促进对神经网络的更有针对性的研究。