{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-binary-variational-autoencoder-for-hashing","title":"A Binary Variational Autoencoder for Hashing","arxiv_id":null,"date":"2019-10-22","proceeding":"Lecture Notes in Computer Science 2019 10","authors":["Francisco Mena","Ricardo Ñanculef"],"abstract":"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.","url_abs":"https://doi.org/10.1007/978-3-030-33904-3_12","url_pdf":"https://link.springer.com/content/pdf/10.1007/978-3-030-33904-3_12.pdf?pdf=inline%20link","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-binary-variational-autoencoder-for-hashing","repo_url":"https://github.com/fmenat/DiscreteVAE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"quantization","task_name":"Quantization"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"text-retrieval","task_name":"Text Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-retrieval-on-20-newsgroups","task":"Text Retrieval","dataset":"20 Newsgroups","model":"B-VAE","rank_in_archive_order":1,"of":3,"metrics":{"Precision@100":"0.441"},"uses_additional_data":false},{"leaderboard":"/sota/text-retrieval-on-20-newsgroups","task":"Text Retrieval","dataset":"20 Newsgroups","model":"VDSH","rank_in_archive_order":3,"of":3,"metrics":{"Precision@100":"0.319"},"uses_additional_data":true},{"leaderboard":"/sota/text-retrieval-on-reuters-21578","task":"Text Retrieval","dataset":"Reuters-21578","model":"B-VAE","rank_in_archive_order":2,"of":3,"metrics":{"Precision@100":"0.698"},"uses_additional_data":false},{"leaderboard":"/sota/text-retrieval-on-reuters-21578","task":"Text Retrieval","dataset":"Reuters-21578","model":"VDSH","rank_in_archive_order":3,"of":3,"metrics":{"Precision@100":"0.556"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}