disentangled-representations 开源项目推荐（121 个）

WisconsinAIVision /MixNMatch

Pytorch implementation of MixNMatch

matthewvowels1 /Awesome-VAEs

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

YannDubs /disentangling-vae

Experiments for understanding disentanglement in VAE latent representations

auspicious3000 /SpeechSplit

Unsupervised Speech Decomposition Via Triple Information Bottleneck

1Konny /Beta-VAE

Pytorch implementation of β-VAE

VQAssessment /DOVER

[ICCV 2023, Official Code] for paper "Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives". Official Weights and Demos provided.

junyanz /VON

[NeurIPS 2018]视觉对象网络：带有解缠3D表示的图像生成。

Schlumberger /joint-vae

Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation 🌟

imlixinyang /HiSD

Code for "Image-to-image Translation via Hierarchical Style Disentanglement" (CVPR 2021 Oral).

1Konny /FactorVAE

Pytorch implementation of FactorVAE proposed in Disentangling by Factorising(http://arxiv.org/abs/1802.05983)

auspicious3000 /AutoPST

Global Rhythm Style Transfer Without Text Transcriptions

kkanshul /finegan

FineGAN: Unsupervised Hierarchical Disentanglement for Fine-grained Object Generation and Discovery

ysymyth /3D-SDN

[NeurIPS 2018] 3D-Aware Scene Manipulation via Inverse Graphics

liaohaofu /adn

ADN：无监督金属文物减少的文物纠缠解网

rozumden /DeFMO

[CVPR 2021] DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

shubhtuls /factored3d

Code release for "Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene"

vineetjohn /linguistic-style-transfer

Neural network parametrized objective to disentangle and transfer style and content in text

xiangwang1223 /disentangled_graph_collaborative_filtering

Disentagnled Graph Collaborative Filtering, SIGIR2020

biggytruck /SpeechSplit2

Official implementation of SpeechSplit2

xrenaa /DisCo

[ICLR2022] Code for "Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View"