Overview
Variational Autoencoders (VAEs) are a class of generative models that combine deep learning with Bayesian inference. Unlike standard autoencoders that map inputs to a single point in a latent space, VAEs map inputs to a distribution (usually a Gaussian).
Key Concepts
- Encoder: Maps the input data to the parameters of a probability distribution (mean and variance) in the latent space.
- Latent Space: A compressed representation of the data where similar items are grouped together.
- Decoder: Samples a point from the latent distribution and reconstructs the original input.
- Reparameterization Trick: A mathematical technique that allows gradients to flow through the random sampling process during training.
Applications
- Image generation and editing.
- Data denoising.
- Molecular discovery in chemistry.
- Anomaly detection.