Density Estimation

412 papers with code • 14 benchmarks • 14 datasets

The goal of Density Estimation is to give an accurate description of the underlying probabilistic density distribution of an observable data set with unknown density.

Source: Contrastive Predictive Coding Based Feature for Automatic Speaker Verification

Libraries

Use these libraries to find Density Estimation models and implementations

Most implemented papers

Denoising Diffusion Probabilistic Models

hojonathanho/diffusion NeurIPS 2020

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

Density estimation using Real NVP

tensorflow/models 27 May 2016

Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning.

Glow: Generative Flow with Invertible 1x1 Convolutions

openai/glow NeurIPS 2018

Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis.

Importance Weighted Autoencoders

AntixK/PyTorch-VAE 1 Sep 2015

The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently proposed generative model pairing a top-down generative network with a bottom-up recognition network which approximates posterior inference.

Masked Autoregressive Flow for Density Estimation

gpapamak/maf NeurIPS 2017

By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow.

MADE: Masked Autoencoder for Distribution Estimation

mgermain/MADE 12 Feb 2015

There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples.

Conditional Image Generation with PixelCNN Decoders

openai/pixel-cnn NeurIPS 2016

This work explores conditional image generation with a new image density model based on the PixelCNN architecture.

Progressive Distillation for Fast Sampling of Diffusion Models

google-research/google-research ICLR 2022

Second, we present a method to distill a trained deterministic diffusion sampler, using many steps, into a new diffusion model that takes half as many sampling steps.

Score-Based Generative Modeling through Stochastic Differential Equations

yang-song/score_sde ICLR 2021

Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9. 89 and FID of 2. 20, a competitive likelihood of 2. 99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.

Point-Set Registration: Coherent Point Drift

neka-nat/probreg 15 May 2009

The goal of point set registration is to assign correspondences between two sets of points and to recover the transformation that maps one point set to the other.