> ML_ARCHITECTURE // VARIATIONAL-AUTOENCODER-VAE_v1.0
Variational Autoencoder (VAE / VQ-VAE)
Probabilistic generative model mapping input data into a parameterized continuous or discrete latent space via the reparameterization trick, regularized using Kullback-Leibler divergence.
Deep Generative Modelsimageaudiotabular
Back to All ArchitecturesArchitecture Overview
Probabilistic generative model mapping input data into a parameterized continuous or discrete latent space via the reparameterization trick, regularized using Kullback-Leibler divergence.
Implementing Libraries
Seminal Papers
Auto-Encoding Variational Bayes (VAE)Diederik P. Kingma, Max Welling (2013) · International Conference on Learning Representations (ICLR)
Architectural Limitations & Constraints
- Requires compatible deep learning framework and hardware acceleration for efficient execution.