Autoencoder is an unsupervised learning model, which can automatically learn data
features from a large number of samples and can act as a dimensionality reduction method …

Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Diffusion-based generative models have demonstrated a capacity for perceptually
impressive synthesis, but can they also be great likelihood-based models? We answer this …

We show that cascaded diffusion models are capable of generating high fidelity images on
the class-conditional ImageNet generation benchmark, without any assistance from auxiliary …

Creating noise from data is easy; creating data from noise is generative modeling. We
present a stochastic differential equation (SDE) that smoothly transforms a complex data …

Denoising diffusion probabilistic models (DDPM) are a class of generative models which
have recently been shown to produce excellent samples. We show that with a few simple …

We introduce a novel method using a new generative model that automatically learns
effective representations of the target and background appearance to detect, segment and …

We present VideoGPT: a conceptually simple architecture for scaling likelihood based
generative modeling to natural videos. VideoGPT uses VQ-VAE that learns downsampled …

We present high quality image synthesis results using diffusion probabilistic models, a class
of latent variable models inspired by considerations from nonequilibrium thermodynamics …

Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep
energy-based models are among competing likelihood-based frameworks for deep …