Cited by 1,011Neural Networks and ApplicationsTopic ModelingImage and Signal Denoising Methods

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A Connection Between Score Matching and Denoising Autoencoders

Vincent, Pascal·2011·Neural Computation

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Abstract

Denoising autoencoders have been previously shown to be competitive alternatives to restricted Boltzmann machines for unsupervised pretraining of each layer of a deep architecture. We show that a simple denoising autoencoder training criterion is equivalent to matching the score (with respect to the data) of a specific energy-based model to that of a nonparametric Parzen density estimator of the data. This yields several useful insights. It defines a proper probabilistic model for the denoising autoencoder technique, which makes it in principle possible to sample from them or rank examples by their energy. It suggests a different way to apply score matching that is related to learning to denoise and does not require computing second derivatives. It justifies the use of tied weights between the encoder and decoder and suggests ways to extend the success of denoising autoencoders to a larger family of energy-based models.

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Authors: Vincent, P.
Affiliations: Université de Montréal (CA)
Corresponding author: Vincent, Pascal
Venue: Neural Computation
Publisher: MIT Press
Published: 2011-07
Volume: 23
Issue: 7
Pages: 1661-1674
ISSN: 0899-7667 (ISSN-L)
1530-888X
DOI: 10.1162/neco_a_00142
OpenAlex: W2013035813
Cited by: 1,011
References: 29
Citations by year: 2026: 131 · 2025: 254 · 2024: 198 · 2023: 126 · 2022: 46 · 2021: 59 · 2020: 52 · 2019: 29 · 2018: 20 · 2017: 22 · 2016: 20 · 2015: 14 · 2014: 9 · 2013: 14 · 2012: 9
FWCI: 11.49
Type: journal article
Language: en
Open access: closed
Locations: Neural Computation (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
Keywords: score matching, denoising autoencoder, energy-based models, unsupervised pre-training, restricted Boltzmann machine, probabilistic models, deep learning architectures
MeSH terms: Computer Simulation, Models, Statistical

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A Connection Between Score Matching and Denoising Autoencoders

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