Papers › A Binary Variational Autoencoder for Hashing

A Binary Variational Autoencoder for Hashing

22 Oct 2019Lecture Notes in Computer Science 2019 10archive 2025-07-28

Francisco Mena, Ricardo Ñanculef

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.

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