We claim that a source of severe failures for Variational Auto-Encoders is the choice of the distribution class used for the observation model.A first theoretical and experimental contribution of the paper is to establish that even in the large sample limit with arbitrarily powerful neural architectures and latent space, the VAE failsif the sharpness of the distribution class does not match the scale of the data.Our second claim is that the distribution sharpness must preferably be learned by the VAE (as opposed to, fixed and optimized offline): Autonomously adjusting this sharpness allows the VAE to dynamically control the trade-off between the optimization of the reconstruction loss and the latent compression. A second empirical contribu...
This paper explores two useful modifications of the recent variational autoencoder (VAE), a popular ...
Likelihood-based generative frameworks are receiving increasing attention in the deep learning commu...
Density estimation, compression, and data generation are crucial tasks in artificial intelligence. V...
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...
Variational auto-encoders (VAEs) are a powerful approach to unsupervised learning. They enable scala...
A key advance in learning generative models is the use of amortized inference distributions that are...
Denoising autoencoders (DAE) are trained to reconstruct their clean inputs with noise injected at th...
In this article, we highlight what appears to be major issue of Variational Autoencoders, evinced fr...
A deep latent variable model is a powerful tool for modelling complex distributions. However, in ord...
Image generative models can learn the distributions of the training data and consequently generate e...
In the past few years Generative models have become an interesting topic in the field of Machine Lea...
Density estimation, compression, and data generation are crucial tasks in artificial intelligence. V...
Thesis (Master's)--University of Washington, 2022In this thesis, we conduct a thorough study of "Var...
Variational autoencoders (VAE) have recently become one of the most interesting developments in deep...
This paper explores two useful modifications of the recent variational autoencoder (VAE), a popular ...
Likelihood-based generative frameworks are receiving increasing attention in the deep learning commu...
Density estimation, compression, and data generation are crucial tasks in artificial intelligence. V...
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...
Variational auto-encoders (VAEs) are a powerful approach to unsupervised learning. They enable scala...
A key advance in learning generative models is the use of amortized inference distributions that are...
Denoising autoencoders (DAE) are trained to reconstruct their clean inputs with noise injected at th...
In this article, we highlight what appears to be major issue of Variational Autoencoders, evinced fr...
A deep latent variable model is a powerful tool for modelling complex distributions. However, in ord...
Image generative models can learn the distributions of the training data and consequently generate e...
In the past few years Generative models have become an interesting topic in the field of Machine Lea...
Density estimation, compression, and data generation are crucial tasks in artificial intelligence. V...
Thesis (Master's)--University of Washington, 2022In this thesis, we conduct a thorough study of "Var...
Variational autoencoders (VAE) have recently become one of the most interesting developments in deep...
This paper explores two useful modifications of the recent variational autoencoder (VAE), a popular ...
Likelihood-based generative frameworks are receiving increasing attention in the deep learning commu...
Density estimation, compression, and data generation are crucial tasks in artificial intelligence. V...