Explore machine learning papers and reviews related to Generative Models. Find insights, analysis, and implementation details.
Ian J. Goodfellow, Jean Pouget-Abadie, +6
How GANs frame generative modeling as a two-player minimax game: a generator turns noise into samples while a discriminator learns to tell real from fake, driving the generator toward the true data distribution.
Diederik P. Kingma, Max Welling
How the variational autoencoder turns an autoencoder into a generative model: an encoder predicts a Gaussian latent distribution, the reparameterization trick makes sampling differentiable, and the ELBO balances reconstruction against a KL prior.
Jonathan Ho, Ajay Jain, +1
How diffusion models learn to generate images by reversing a gradual noising process — the foundation of Stable Diffusion, DALL-E, and modern image generation.
Yaron Lipman, Ricky T. Q. Chen, +2
How Flow Matching simplifies generative modeling by learning straight transport paths from noise to data — faster sampling, simpler training, and the foundation of modern generation systems.
Robin Rombach, Andreas Blattmann, +3
How Latent Diffusion Models made high-resolution image generation practical by moving diffusion to a compressed latent space — the architecture behind Stable Diffusion.