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.
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by Juno Kim,Jae... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2312.01133.pdfDeeper Inquiries