{"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/semantic-cluster-unary-loss-for-efficient","title":"Semantic Cluster Unary Loss for Efficient Deep Hashing","arxiv_id":"1805.08705","date":"2018-05-15","proceeding":null,"authors":["Shifeng Zhang","Jianmin Li","Bo Zhang"],"abstract":"Hashing method maps similar data to binary hashcodes with smaller hamming\ndistance, which has received a broad attention due to its low storage cost and\nfast retrieval speed. With the rapid development of deep learning, deep hashing\nmethods have achieved promising results in efficient information retrieval.\nMost of the existing deep hashing methods adopt pairwise or triplet losses to\ndeal with similarities underlying the data, but the training is difficult and\nless efficient because $O(n^2)$ data pairs and $O(n^3)$ triplets are involved.\nTo address these issues, we propose a novel deep hashing algorithm with unary\nloss which can be trained very efficiently. We first of all introduce a Unary\nUpper Bound of the traditional triplet loss, thus reducing the complexity to\n$O(n)$ and bridging the classification-based unary loss and the triplet loss.\nSecond, we propose a novel Semantic Cluster Deep Hashing (SCDH) algorithm by\nintroducing a modified Unary Upper Bound loss, named Semantic Cluster Unary\nLoss (SCUL). The resultant hashcodes form several compact clusters, which means\nhashcodes in the same cluster have similar semantic information. We also\ndemonstrate that the proposed SCDH is easy to be extended to semi-supervised\nsettings by incorporating the state-of-the-art semi-supervised learning\nalgorithms. Experiments on large-scale datasets show that the proposed method\nis superior to state-of-the-art hashing algorithms.","url_abs":"http://arxiv.org/abs/1805.08705v2","url_pdf":"http://arxiv.org/pdf/1805.08705v2.pdf","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":"semantic-cluster-unary-loss-for-efficient","repo_url":"https://github.com/zsffq999/SCDH","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-hashing","task_name":"Deep Hashing"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":null,"task_name":"Triplet"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}