{"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/noise-resistant-deep-metric-learning-with","title":"Noise-resistant Deep Metric Learning with Ranking-based Instance Selection","arxiv_id":"2103.16047","date":"2021-03-30","proceeding":"CVPR 2021 1","authors":["Chang Liu","Han Yu","Boyang Li","Zhiqi Shen","Zhanning Gao","Peiran Ren","Xuansong Xie","Lizhen Cui","Chunyan Miao"],"abstract":"The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving robustness to noisy labels in classification tasks, the problem of noisy labels in deep metric learning (DML) remains open. In this paper, we propose a noise-resistant training technique for DML, which we name Probabilistic Ranking-based Instance Selection with Memory (PRISM). PRISM identifies noisy data in a minibatch using average similarity against image features extracted by several previous versions of the neural network. These features are stored in and retrieved from a memory bank. To alleviate the high computational cost brought by the memory bank, we introduce an acceleration method that replaces individual data points with the class centers. In extensive comparisons with 12 existing approaches under both synthetic and real-world label noise, PRISM demonstrates superior performance of up to 6.06% in Precision@1.","url_abs":"https://arxiv.org/abs/2103.16047v2","url_pdf":"https://arxiv.org/pdf/2103.16047v2.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":"noise-resistant-deep-metric-learning-with","repo_url":"https://github.com/alibaba-edu/Ranking-based-Instance-Selection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"metric-learning","task_name":"Metric Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2103.16047","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16047"}},"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":"deterministic:regex_extraction","url":"https://github.com/alibaba-edu/Ranking-based-Instance-Selection","reach":null}],"summary":{"ran":1,"unverified":1},"by_repo_kind":{"official":{"samples":2,"ran":1,"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":0,"samples":[{"code_sha256_prefix":"d5ea45050aa7cd97","entry":"Registry","repo":"alibaba-edu/Ranking-based-Instance-Selection","repo_kind":"official","path":"ret_benchmark/losses/PRISM.py","file_url":"https://github.com/alibaba-edu/Ranking-based-Instance-Selection/blob/HEAD/ret_benchmark/losses/PRISM.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d5ea45050aa7cd97"}},{"code_sha256_prefix":"daa1d034e7b8f4ff","entry":"PRISM","repo":"alibaba-edu/Ranking-based-Instance-Selection","repo_kind":"official","path":"ret_benchmark/losses/PRISM.py","file_url":"https://github.com/alibaba-edu/Ranking-based-Instance-Selection/blob/HEAD/ret_benchmark/losses/PRISM.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"daa1d034e7b8f4ff"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}