A Binary Variational Autoencoder for Hashing

  • Francisco MenaEmail author
  • Ricardo ÑanculefEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)


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.


Hashing Variational autoencoders Deep learning Gumbel-Softmax distribution Neural information retrieval 



F. Mena thanks the Programa de Iniciación Científica PIIC-DGIP of the Federico Santa María University for funding this work.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Federico Santa María UniversitySantiagoChile

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