Dynamical Variational Autoencoders: A Comprehensive Review - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Foundations and Trends in Machine Learning Year : 2021

Dynamical Variational Autoencoders: A Comprehensive Review

Abstract

Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent space learned in an unsupervised manner. In the original VAE model, the input data vectors are processed independently. Recently, a series of papers have presented different extensions of the VAE to process sequential data, which model not only the latent space but also the temporal dependencies within a sequence of data vectors and corresponding latent vectors, relying on recurrent neural networks or state-space models. In this paper, we perform a literature review of these models. We introduce and discuss a general class of models, called dynamical variational autoencoders (DVAEs), which encompasses a large subset of these temporal VAE extensions. Then, we present in detail seven recently proposed DVAE models, with an aim to homogenize the notations and presentation lines, as well as to relate these models with existing classical temporal models. We have reimplemented those seven DVAE models and present the results of an experimental benchmark conducted on the speech analysis-resynthesis task (the PyTorch code is made publicly available). The paper concludes with a discussion on important issues concerning the DVAE class of models and future research guidelines.
Fichier principal
Vignette du fichier
Girin_at_al_DVAE_review_2021_arXiv_version.pdf (3.75 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-02926215 , version 1 (18-01-2022)
hal-02926215 , version 2 (05-07-2022)

Identifiers

Cite

Laurent Girin, Simon Leglaive, Xiaoyu Bie, Julien Diard, Thomas Hueber, et al.. Dynamical Variational Autoencoders: A Comprehensive Review. Foundations and Trends in Machine Learning, 2021, 15 (1-2), pp.1-175. ⟨10.1561/2200000089⟩. ⟨hal-02926215v2⟩
1081 View
1410 Download

Altmetric

Share

Gmail Facebook X LinkedIn More