{"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/link-and-code-fast-indexing-with-graphs-and","title":"Link and code: Fast indexing with graphs and compact regression codes","arxiv_id":"1804.09996","date":"2018-04-26","proceeding":"CVPR 2018 6","authors":["Matthijs Douze","Alexandre Sablayrolles","Hervé Jégou"],"abstract":"Similarity search approaches based on graph walks have recently attained\noutstanding speed-accuracy trade-offs, taking aside the memory requirements. In\nthis paper, we revisit these approaches by considering, additionally, the\nmemory constraint required to index billions of images on a single server. This\nleads us to propose a method based both on graph traversal and compact\nrepresentations. We encode the indexed vectors using quantization and exploit\nthe graph structure to refine the similarity estimation.\n  In essence, our method takes the best of these two worlds: the search\nstrategy is based on nested graphs, thereby providing high precision with a\nrelatively small set of comparisons. At the same time it offers a significant\nmemory compression. As a result, our approach outperforms the state of the art\non operating points considering 64-128 bytes per vector, as demonstrated by our\nresults on two billion-scale public benchmarks.","url_abs":"http://arxiv.org/abs/1804.09996v2","url_pdf":"http://arxiv.org/pdf/1804.09996v2.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":"link-and-code-fast-indexing-with-graphs-and","repo_url":"https://github.com/facebookresearch/faiss","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"link-and-code-fast-indexing-with-graphs-and","repo_url":"https://github.com/NGDSystems/faiss","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"link-and-code-fast-indexing-with-graphs-and","repo_url":"https://github.com/NJU-yasuo/faiss_t","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"link-and-code-fast-indexing-with-graphs-and","repo_url":"https://github.com/PhilipBAdams/faiss-learned-termination-prior-weighted","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"link-and-code-fast-indexing-with-graphs-and","repo_url":"https://github.com/architecture-research-group/ae-asplo25-iks-faiss","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"link-and-code-fast-indexing-with-graphs-and","repo_url":"https://github.com/gauenk/faiss_fork","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"link-and-code-fast-indexing-with-graphs-and","repo_url":"https://github.com/hartb/faiss-gpu-feedstock","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"link-and-code-fast-indexing-with-graphs-and","repo_url":"https://github.com/junjya/faiss","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-similarity-search","task_name":"Image Similarity Search"},{"task_slug":"quantization","task_name":"Quantization"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1804.09996","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}