Exploring Generative Models in Hyperbolic Space

Abstract

Generative models are powerful machine learning models that have the ability to probabilistically generate unique, realistic samples of data. These models can generate anything from hyper-realistic images of faces to 3D models of chairs. Recent publicly-available generative models such as ChatGPT and DALL-E have gained widespread attention and usage. However, generative models often have high-dimensional latent space representations of the data, which results in the latent space vectors used to generate outputs being difficult to interpret. In this paper, we introduce an application for exploring generative models in hyperbolic space. We adopt the hyperboloid model of hyperbolic geometry and implement a regular tiling system for this model. We implement the hyperbolic world in the Unity game engine and link the hyperbolic world with a Flask server that produces output images from LAFITE, a state-of-the-art generative model. Finally, we run simulated experiments to evaluate the effectiveness of our system as a tool for human-in-the-loop optimization of generative models of varying dimensions.