Generative AI: Latent Spaces Implementation

Dimensionality and Latent Space implementation using PCA and VAEs

Before we start, we learn more about what latent spaces are and what role they play in Generative AI. Let us first understand what dimensionality and High and Low dimensional spaces are. This article includes a simple “Library of books” analogy to explain Dimensionality and Latent space concepts and includes complete code and links to Colab Notebooks for dimensionality reduction using the PCA method and Latent Space implementation using VAEs.

Dimensionality

An Introduction

Dimensionality can be thought of as the number of characteristics or attributes used to describe an object.

High Dimensionality means that each data point is described by a large number of features.

a. High-dimensional data can capture intricate patterns and relationships due to the richness of features.

b. t often leads to the curse of dimensionality, where the volume of the space increases exponentially with the number of dimensions, making it difficult to analyze and visualize.

c. In high-dimensional spaces, data points tend to be sparse, meaning they are far apart from each other. This sparsity can make it harder to find meaningful similarities or patterns.

Low dimensionality means that each data point is described by a small number of features.