Variational Autoencoders (VAEs)
Interactively explore how VAEs encode data into a latent space and reconstruct it, balancing reconstruction and regularization.
VAE Latent Space Simulation
Educational VAE Sandbox
Explanation
- Encoder: Maps input data to a distribution (mean and variance) in latent space.
- Decoder: Maps a sampled latent point back to the data space.
- Reparameterization Trick: Allows backpropagation through random sampling:
z = μ + σ * ε - Loss Functions: Combines reconstruction loss and KL divergence to regularize latent space.
Drag the orange latent point or click Random Sample z to see how reconstruction changes.
KL divergence is visualized as distance from center.
KL divergence is visualized as distance from center.