• Author(s): Kieran A. Murphy, Sam Dillavou, Dani S. Bassett

This paper investigates the information content of representation spaces in the context of disentanglement using Variational Autoencoder (VAE) ensembles. Disentanglement aims to separate the underlying factors of variation in data, which is crucial for various machine learning applications. The study focuses on understanding how different VAE models capture and represent information in their latent spaces and how these representations contribute to disentanglement.

The research employs an ensemble of VAE models to analyze the representation spaces. By comparing the information content across different models, the study aims to identify the characteristics that lead to effective disentanglement. The methodology involves training multiple VAE models on the same dataset and evaluating their performance using established disentanglement metrics. The ensemble approach allows for a comprehensive analysis of the latent spaces, providing insights into the variability and robustness of the representations.

The results indicate that certain VAE models consistently produce more disentangled representations, highlighting the importance of model selection in achieving effective disentanglement. The study also explores the relationship between the information content of the latent spaces and the quality of disentanglement. It is observed that models with higher information content tend to perform better in disentangling the underlying factors of variation.

Furthermore, the paper discusses the implications of these findings for the design and training of VAE models. By understanding the factors that contribute to effective disentanglement, researchers can develop more robust and efficient models for various applications, including generative modeling, data compression, and feature extraction. This paper provides a detailed analysis of the information content in representation spaces for disentanglement using VAE ensembles. The findings offer valuable insights into the characteristics of effective disentanglement and highlight the importance of model selection and training strategies. This research contributes to the ongoing efforts to improve the performance and applicability of VAE models in machine learning.