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Density Estimation

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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/khangt1k25/MADE

MADE: Masked Autoencoder for Distribution Estimation

https://github.com/kkleidal/gatedpixelcnnpytorch

Conditional Image Generation with PixelCNN Decoders

https://github.com/kmzzhang/nbi

Masked Autoregressive Flow for Density Estimation

https://github.com/koritsky/pointconv

PointConv: Deep Convolutional Networks on 3D Point Clouds

https://github.com/kpandey008/DiffuseVAE

Denoising Diffusion Probabilistic Models

https://github.com/krasserm/perceiver-io

General-purpose, long-context autoregressive modeling with Perceiver AR

https://github.com/krutikabapat/Crowd_Counting

Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking

https://github.com/la3ma/IWAE-torch

Importance Weighted Autoencoders

https://github.com/labmlai/annotated_deep_learning_paper_implementations

Denoising Diffusion Probabilistic Models

https://github.com/layer6ai-labs/dgm_manifold_survey

Diagnosing and Fixing Manifold Overfitting in Deep Generative Models

https://github.com/layer6ai-labs/uomh

Diagnosing and Fixing Manifold Overfitting in Deep Generative Models

https://github.com/lee-group-cmu/RFCDE

RFCDE: Random Forests for Conditional Density Estimation

https://github.com/leequant761/Gumbel-SSVAE

The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

https://github.com/leffff/FlowModels

Flow Matching for Generative Modeling

https://github.com/lenz3000/ffjord-path

FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

https://github.com/li012589/NeuralRG

Density estimation using Real NVP

https://github.com/liaopeiyuan/pndm

Score-Based Generative Modeling through Stochastic Differential Equations

https://github.com/lifeitech/fce-2d

Masked Autoregressive Flow for Density Estimation

https://github.com/lim0606/pytorch-ardae-rl

AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

https://github.com/lim0606/pytorch-ardae-vae

AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

https://github.com/lmnt-com/diffwave

Denoising Diffusion Probabilistic Models

https://github.com/lmnt-com/wavegrad

Denoising Diffusion Probabilistic Models

https://github.com/logchan/dde

Learning Generative Models using Denoising Density Estimators

https://github.com/lucidrains/ddpm-proteins

Denoising Diffusion Probabilistic Models

https://github.com/lucidrains/denoising-diffusion-pytorch

Denoising Diffusion Probabilistic Models

https://github.com/lucidrains/imagen-pytorch

Denoising Diffusion Probabilistic Models

https://github.com/lucidrains/make-a-video-pytorch

Denoising Diffusion Probabilistic Models

https://github.com/lucidrains/perceiver-ar-pytorch

General-purpose, long-context autoregressive modeling with Perceiver AR

https://github.com/luping-liu/PNDM

Score-Based Generative Modeling through Stochastic Differential Equations

https://github.com/lupvasile/hd3

Hierarchical Discrete Distribution Decomposition for Match Density Estimation

https://github.com/lvyufeng/denoising-diffusion-mindspore

Denoising Diffusion Probabilistic Models

https://github.com/lyqcom/BiRealNet

CNN-based Density Estimation and Crowd Counting: A Survey

https://github.com/mackelab/nflows-nofork

Neural Spline Flows

https://github.com/madhubabuv/TightIWAE

Importance Weighted Autoencoders

https://github.com/madsendennis/template-registration-with-scala

Point-Set Registration: Coherent Point Drift

https://github.com/mahkons/flows

Density estimation using Real NVP

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