Latent Space Analysis, Visualization, and Interpretability in Deep Learning: Systematic Review

Abstract

Latent space representations play a key role in modern machine learning models. This systematic review focuses on latent space research with emphasis on analysis methods, dimension reduction, visualization approaches, data types, and study objectives. Both supervised and unsupervised experimental settings are considered. The reviewed studies report diverse outcomes, including performance improvement, accuracy gains, and the discovery of meaningful latent structures. In addition, deep clustering methods are shown to be widely used across various application domains. © 2026 IEEE.