Add to ORKGVariational Autoencoders are one of the most commonly used generative models, particularly for image data. A prominent difficulty in training VAEs is data that is supported on a lower-dimensional manifold. Recent work by Dai and Wipf (2020) proposes a two-stage training algorithm for VAEs, based on a conjecture that in standard VAE training the generator will converge to a solution with 0 variance which is correctly supported on the ground truth manifold. They gave partial support for that conjecture by showing that some optima of the VAE loss do satisfy this property, but did not analyze the training dynamics. In this paper, we show that for linear encoders/decoders, the conjecture is true-that is the VAE training does recover a generator ...
Variational auto-encoders (VAEs) are a powerful approach to unsupervised learning. They enable scala...
In the past few years Generative models have become an interesting topic in the field of Machine Lea...
In the Variational Autoencoder (VAE), the variational posterior often aligns closely with the prior,...
In this article, we highlight what appears to be major issue of Variational Autoencoders, evinced fr...
Variational autoencoders (VAEs) are a popular framework for modeling complex data distributions; the...
We claim that a source of severe failures for Variational Auto-Encoders is the choice of the distrib...
Variational autoencoders (VAEs) are a popular class of deep generative models with many variants and...
The success of modern machine learning algorithms depends crucially on efficient data representation...
By composing graphical models with deep learning architectures, we learn generative models with the ...
Image generative models can learn the distributions of the training data and consequently generate e...
Discrete variational auto-encoders (VAEs) are able to represent semantic latent spaces in generative...
We propose a Gaussian manifold variational auto-encoder (GM-VAE) whose latent space consists of a se...
Autoencoders exhibit impressive abilities to embed the data manifold into a low-dimensional latent s...
A key advance in learning generative models is the use of amortized inference distributions that are...
Unsupervised learning (UL) is a class of machine learning (ML) that learns data, reduces dimensional...
Variational auto-encoders (VAEs) are a powerful approach to unsupervised learning. They enable scala...
In the past few years Generative models have become an interesting topic in the field of Machine Lea...
In the Variational Autoencoder (VAE), the variational posterior often aligns closely with the prior,...
In this article, we highlight what appears to be major issue of Variational Autoencoders, evinced fr...
Variational autoencoders (VAEs) are a popular framework for modeling complex data distributions; the...
We claim that a source of severe failures for Variational Auto-Encoders is the choice of the distrib...
Variational autoencoders (VAEs) are a popular class of deep generative models with many variants and...
The success of modern machine learning algorithms depends crucially on efficient data representation...
By composing graphical models with deep learning architectures, we learn generative models with the ...
Image generative models can learn the distributions of the training data and consequently generate e...
Discrete variational auto-encoders (VAEs) are able to represent semantic latent spaces in generative...
We propose a Gaussian manifold variational auto-encoder (GM-VAE) whose latent space consists of a se...
Autoencoders exhibit impressive abilities to embed the data manifold into a low-dimensional latent s...
A key advance in learning generative models is the use of amortized inference distributions that are...
Unsupervised learning (UL) is a class of machine learning (ML) that learns data, reduces dimensional...
Variational auto-encoders (VAEs) are a powerful approach to unsupervised learning. They enable scala...
In the past few years Generative models have become an interesting topic in the field of Machine Lea...
In the Variational Autoencoder (VAE), the variational posterior often aligns closely with the prior,...