Density Estimation
420 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
Benchmarks
These leaderboards are used to track progress in Density Estimation
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Libraries
Use these libraries to find Density Estimation models and implementationsDatasets
CIFAR-10
MNIST
CelebA-HQ
BSD
Caltech-101
Omniglot
JHU-CROWD++
UCF-CC-50
Silhouettes
Most implemented papers
Denoising Diffusion Probabilistic Models
hojonathanho/diffusion
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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
Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning.
Glow: Generative Flow with Invertible 1x1 Convolutions
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
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
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
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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
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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
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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
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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.
PointConv: Deep Convolutional Networks on 3D Point Clouds
DylanWusee/pointconv
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CVPR 2019
Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure.
