eXact-Prior Variational Autoencoder (X-VAE): Learning Data-Adaptive Gaussian Mixture Priors for Latent Distributions
arXiv:2607.01275v1 Announce Type: cross Abstract: Variational Autoencoders (VAEs) commonly assume a standard isotropic Gaussian prior over the latent space, an assumption that often fails to capture the true distribution of latent representations for complex datasets. This mismatch can limit reconstruction accuracy, reduce sample quality, and constrain the expressive power of the learned latent space. We propose the eXact-Prior Variational Autoencoder (X-VAE), a framework that replaces the conventional standard normal prior with a Gaussian prior derived from the latent representations of a pre
Ongoing research in AI aims to address fundamental limitations of current generative models, with focus on improving representation learning and data efficiency.
Improving Variational Autoencoders by learning data-adaptive priors could lead to more robust and accurate generative AI models, impacting various downstream applications.
The assumption of a static Gaussian prior in VAEs is challenged, potentially leading to more sophisticated and expressive latent space representations.
- · AI researchers
- · Generative AI applications
- · Data scientists
- · Models reliant on simplistic latent representations
Improved performance of generative models in tasks like anomaly detection, data augmentation, and content creation.
More efficient and accurate learning on complex and diverse datasets with fewer computational resources.
Accelerated development of AI agents and systems requiring highly nuanced and data-adaptive internal representations of the world.
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Read at arXiv cs.LG