Disentangling Disentanglement in Variational Autoencoders
Mathieu et al.: http://proceedings.mlr.press/v97/mathieu19a.html
#ArtificialIntelligence #DeepLearning #VariationalAutoencoders #VAE
Mathieu et al.: http://proceedings.mlr.press/v97/mathieu19a.html
#ArtificialIntelligence #DeepLearning #VariationalAutoencoders #VAE
PMLR
Disentangling Disentanglement in Variational Autoencoders
We develop a generalisation of disentanglement in variational autoencoders (VAEs)—decomposition of the latent representation—characterising it as the fulfilm...
Reweighted Expectation Maximization
Adji B. Dieng and John Paisley: https://arxiv.org/abs/1906.05850
Code: https://github.com/adjidieng/REM
#ArtificialIntelligence #MachineLearning #VariationalAutoEncoders #VAEs
Adji B. Dieng and John Paisley: https://arxiv.org/abs/1906.05850
Code: https://github.com/adjidieng/REM
#ArtificialIntelligence #MachineLearning #VariationalAutoEncoders #VAEs
"Variational Autoencoders and Nonlinear ICA: A Unifying Framework"
Khemakhem et al.: https://arxiv.org/abs/1907.04809
#MachineLearning #GenerativeModels #VariationalAutoencoders
Khemakhem et al.: https://arxiv.org/abs/1907.04809
#MachineLearning #GenerativeModels #VariationalAutoencoders
arXiv.org
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often,...
Disentangling Disentanglement in Variational Autoencoders
Mathieu et al.: http://proceedings.mlr.press/v97/mathieu19a.html
#DeepLearning #VariationalAutoencoders #VAE
Mathieu et al.: http://proceedings.mlr.press/v97/mathieu19a.html
#DeepLearning #VariationalAutoencoders #VAE
PMLR
Disentangling Disentanglement in Variational Autoencoders
We develop a generalisation of disentanglement in variational autoencoders (VAEs)—decomposition of the latent representation—characterising it as the fulfilm...
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Khemakhem et al.: https://arxiv.org/abs/1907.04809
#MachineLearning #GenerativeModels #VariationalAutoencoders
Khemakhem et al.: https://arxiv.org/abs/1907.04809
#MachineLearning #GenerativeModels #VariationalAutoencoders
arXiv.org
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often,...