VAE vs GAN vs Diffusion - Interactive Visualization

Three families dominate generative modeling. VAEs maximize a variational lower bound on likelihood, producing smooth latent spaces but somewhat blurry samples due to the reconstruction-regularization tradeoff. GANs are trained adversarially, producing sharp samples but suffering from mode collapse and training instability. Diffusion models iteratively denoise Gaussian noise and currently produce the best sample quality, but require hundreds of forward passes at inference time.

• See VAE architecture: encoder to latent distribution, reparameterization trick, decoder to reconstruction - all trained end-to-end
• Watch GAN training dynamics: the generator and discriminator loss curves and the adversarial dance between them
• See diffusion forward process: clean image progressively corrupted with Gaussian noise across T timesteps
• Compare sample quality across all three: diffusion wins on sharpness, VAE has the best-structured latent space for interpolation
• Understand training stability rankings: VAE (stable) vs diffusion (stable) vs GAN (often diverges without tricks)

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