Core Concepts
The author proposes a modified VAE framework, t3VAE, utilizing heavy-tailed models to better fit real-world datasets by incorporating Student’s t-distributions. The approach aims to address over-regularization issues and improve the generation of low-density regions.
Abstract
The content introduces the t3VAE framework, emphasizing its use of heavy-tailed models like Student’s t-distributions to enhance data fitting. It explores the theoretical background, practical applications in image reconstruction and generation tasks, and comparisons with other VAE models on various datasets.
The variational autoencoder (VAE) is a popular model for learning latent data representations. The Gaussian VAE's limitations in capturing complex latent structures led to the proposal of t3VAE.
t3VAE incorporates Student’s t-distributions for prior, encoder, and decoder, aiming to better fit real-world datasets with heavy-tailed behavior.
By replacing KL divergence with γ-power divergence in the objective function, t3VAE demonstrates superior performance in generating low-density regions on synthetic and real datasets like CelebA and CIFAR-100.
Comparative experiments show that t3VAE outperforms Gaussian VAE and other alternative models in terms of image quality, especially for rare features or imbalanced data distributions.
Stats
The variational autoencoder (VAE) is a popular probabilistic generative model.
The proposed t3VAE framework utilizes heavy-tailed models like Student’s t-distributions.
Experiments demonstrate that t3VAE outperforms Gaussian VAE and other alternative models.
Quotes
"The Gaussian tail often decays too quickly to effectively accommodate the encoded points."
"t3VAE demonstrates superior generation of low-density regions when trained on heavy-tailed synthetic data."