  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.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition