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Density Estimation

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하위 태스크: 1
평가표: 14
보존된 평가 기록: 65

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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ssumin6/real_nvp

Density estimation using Real NVP

https://github.com/stephengreen/lfi-gw

Complete parameter inference for GW150914 using deep learning

https://github.com/taesung89/real-nvp

Density estimation using Real NVP

https://github.com/talreiss/Mean-Shifted-Anomaly-Detection

An Attribute-based Method for Video Anomaly Detection

https://github.com/talreiss/PANDA

An Attribute-based Method for Video Anomaly Detection

https://github.com/taohan10200/steerer

DR.VIC: Decomposition and Reasoning for Video Individual Counting

https://github.com/tbrx/compiled-inference

MADE: Masked Autoencoder for Distribution Estimation

https://github.com/tcapelle/Diffusion-Models-pytorch

Denoising Diffusion Probabilistic Models

https://github.com/tcl9876/denoising_synthesis

Denoising Diffusion Probabilistic Models

https://github.com/teapearce/conditional_diffusion_mnist

Denoising Diffusion Probabilistic Models

https://github.com/tensorflow/models

Density estimation using Real NVP

https://github.com/tensorflow/models/tree/master/research/rebar

The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

https://github.com/tensorflow/probability

Masked Autoregressive Flow for Density Estimation

https://github.com/teticio/audio-diffusion

Denoising Diffusion Probabilistic Models

https://github.com/thu-ml/implicit-normalizing-flows

Residual Flows for Invertible Generative Modeling

https://github.com/tiwarylab/DDPM_REMD

Denoising Diffusion Probabilistic Models

https://github.com/tobiasbernecker/PointSetRegistration

Point-Set Registration: Coherent Point Drift

https://github.com/tommyod/KDEpy

Fast Nonparametric Conditional Density Estimation

https://github.com/tqch/ddpm-torch

Denoising Diffusion Probabilistic Models

https://github.com/tuatruog/astrocompress

PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications

https://github.com/unibas-gravis/gingr

Point-Set Registration: Coherent Point Drift

https://github.com/vainf/diff-pruning

Denoising Diffusion Probabilistic Models

https://github.com/viniciusmikuni/caloscorev2

Progressive Distillation for Fast Sampling of Diffusion Models

https://github.com/viniciusmikuni/gsgm

Progressive Distillation for Fast Sampling of Diffusion Models

https://github.com/vinits5/learning3d

PointConv: Deep Convolutional Networks on 3D Point Clouds

https://github.com/vocong25/gated_pixelcnn

Conditional Image Generation with PixelCNN Decoders

https://github.com/vvvm23/ddpm

Denoising Diffusion Probabilistic Models

https://github.com/vvvm23/glow

Glow: Generative Flow with Invertible 1x1 Convolutions

https://github.com/w86763777/pytorch-ddpm

Denoising Diffusion Probabilistic Models

https://github.com/wedeling/EasySurrogate

The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables

https://github.com/wjy5446/pytorch-Real-NVP

Density estimation using Real NVP

https://github.com/wufan-tb/gmm_dae

Video Anomaly Detection by Estimating Likelihood of Representations

https://github.com/xiangyu-liu/Computer-Network

Importance Weighted Autoencoders

https://github.com/xiaoiker/meta_dpm

Denoising Diffusion Probabilistic Models

https://github.com/xiaosheng-zhao/st4diffusion

Denoising Diffusion Probabilistic Models

https://github.com/xlwan/KRnet

Density estimation using Real NVP

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.