{"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/deep-randomized-ensembles-for-metric-learning","title":"Deep Randomized Ensembles for Metric Learning","arxiv_id":"1808.04469","date":"2018-08-13","proceeding":"ECCV 2018 9","authors":["Hong Xuan","Richard Souvenir","Robert Pless"],"abstract":"Learning embedding functions, which map semantically related inputs to nearby\nlocations in a feature space supports a variety of classification and\ninformation retrieval tasks. In this work, we propose a novel, generalizable\nand fast method to define a family of embedding functions that can be used as\nan ensemble to give improved results. Each embedding function is learned by\nrandomly bagging the training labels into small subsets. We show experimentally\nthat these embedding ensembles create effective embedding functions. The\nensemble output defines a metric space that improves state of the art\nperformance for image retrieval on CUB-200-2011, Cars-196, In-Shop Clothes\nRetrieval and VehicleID.","url_abs":"http://arxiv.org/abs/1808.04469v2","url_pdf":"http://arxiv.org/pdf/1808.04469v2.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":"deep-randomized-ensembles-for-metric-learning","repo_url":"https://github.com/littleredxh/DREML","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"metric-learning","task_name":"Metric Learning"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.04469","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.04469"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/littleredxh/DREML","reach":null}],"summary":{"ran_honours":1,"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"da7aef57c668f3a9","entry":"createID","repo":"littleredxh/DREML","repo_kind":"official","path":"_code/Utils.py","file_url":"https://github.com/littleredxh/DREML/blob/HEAD/_code/Utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"da7aef57c668f3a9"}},{"code_sha256_prefix":"362ce713fb7e7a51","entry":"recall2","repo":"littleredxh/DREML","repo_kind":"official","path":"_code/Utils.py","file_url":"https://github.com/littleredxh/DREML/blob/HEAD/_code/Utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"362ce713fb7e7a51"}},{"code_sha256_prefix":"e4db923bafa25005","entry":"recall","repo":"littleredxh/DREML","repo_kind":"official","path":"_code/Utils.py","file_url":"https://github.com/littleredxh/DREML/blob/HEAD/_code/Utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e4db923bafa25005"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}