From Entanglement to Disentanglement: Comparing Traditional VAE and Modified Beta-VAE Performance

Abstract

This paper evaluates the effectiveness of advanced Variational Autoencoder (VAE) models in overcoming latent space entanglement and insufficient disentanglement, common issues in traditional VAEs. Traditional VAEs often face challenges in separating distinct features within the latent space, leading to entangled representations that hinder interpretability and compression efficiency. The advanced VAE models examined in this study address these issues by enhancing disentanglement, which results in clearer separation of latent factors and more interpretable representations. However, this improvement in disentanglement may result in a trade-off with reconstruction quality. The article shows that, while these sophisticated models improve disentanglement, they may also have worse reconstruction quality than classic VAEs. The findings highlight the necessity of hyperparameter optimization in successfully navigating this trade-off. Future study should investigate novel model architectures and hyperparameter optimization strategies to optimize the balance of disentanglement and reconstruction quality. Overall, the research emphasizes the ability of advanced VAE models to generate more interpretable representations and the importance of careful tuning to resolve the inherent trade-offs.