Abstract
Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions.
An Introduction to Variational Autoencoders
Related papers
Ranked by semantic similarity β how closely each paper's abstract matches this one (100% = near-identical topic).
- An Introduction to Discrete Variational Autoencoders (2025)80% match
- Asymmetric Variational Autoencoders (2017)74% match
- Tutorial on Variational Autoencoders (2016)74% match
- Variational Laplace Autoencoders (2022)73% match
- Variational Autoencoders and Nonlinear ICA: A Unifying Framework (2019)72% match
- Inducing Interpretable Representations with Variational Autoencoders (2016)72% match
- Towards Deeper Understanding of Variational Autoencoding Models (2017)72% match
- Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks (2017)72% match