A Binary Variational Autoencoder for Hashing

Francisco Mena, Ricardo Ñanculef

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

© Springer Nature Switzerland AG 2019. Searching a large dataset to find elements that are similar to a sample object is a fundamental problem in computer science. Hashing algorithms deal with this problem by representing data with similarity-preserving binary codes that can be used as indices into a hash table. Recently, it has been shown that variational autoencoders (VAEs) can be successfully trained to learn such codes in unsupervised and semi-supervised scenarios. In this paper, we show that a variational autoencoder with binary latent variables leads to a more natural and effective hashing algorithm that its continuous counterpart. The model reduces the quantization error introduced by continuous formulations but is still trainable with standard back-propagation. Experiments on text retrieval tasks illustrate the advantages of our model with respect to previous art.
Original languageEnglish
Title of host publicationA Binary Variational Autoencoder for Hashing
Pages131-141
Number of pages11
ISBN (Electronic)9783030339036
DOIs
Publication statusPublished - 1 Jan 2019
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2019 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11896 LNCS
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/19 → …

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  • Cite this

    Mena, F., & Ñanculef, R. (2019). A Binary Variational Autoencoder for Hashing. In A Binary Variational Autoencoder for Hashing (pp. 131-141). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11896 LNCS). https://doi.org/10.1007/978-3-030-33904-3_12