{"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/ranksim-ranking-similarity-regularization-for","title":"RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression","arxiv_id":"2205.15236","date":"2022-05-30","proceeding":null,"authors":["Yu Gong","Greg Mori","Frederick Tung"],"abstract":"Data imbalance, in which a plurality of the data samples come from a small proportion of labels, poses a challenge in training deep neural networks. Unlike classification, in regression the labels are continuous, potentially boundless, and form a natural ordering. These distinct features of regression call for new techniques that leverage the additional information encoded in label-space relationships. This paper presents the RankSim (ranking similarity) regularizer for deep imbalanced regression, which encodes an inductive bias that samples that are closer in label space should also be closer in feature space. In contrast to recent distribution smoothing based approaches, RankSim captures both nearby and distant relationships: for a given data sample, RankSim encourages the sorted list of its neighbors in label space to match the sorted list of its neighbors in feature space. RankSim is complementary to conventional imbalanced learning techniques, including re-weighting, two-stage training, and distribution smoothing, and lifts the state-of-the-art performance on three imbalanced regression benchmarks: IMDB-WIKI-DIR, AgeDB-DIR, and STS-B-DIR.","url_abs":"https://arxiv.org/abs/2205.15236v2","url_pdf":"https://arxiv.org/pdf/2205.15236v2.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":"ranksim-ranking-similarity-regularization-for","repo_url":"https://github.com/BorealisAI/ranksim-imbalanced-regression","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-imbalanced-regression","task_name":"Deep imbalanced regression"},{"task_slug":"inductive-bias","task_name":"Inductive Bias"},{"task_slug":"sts","task_name":"STS"},{"task_slug":null,"task_name":"STS-B"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2205.15236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.15236"}},"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/BorealisAI/ranksim-imbalanced-regression","reach":null}],"summary":{"ran":1,"ran_draft_wrong":2,"unverified":1},"by_repo_kind":{"official":{"samples":4,"ran":3,"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":4,"samples":[{"code_sha256_prefix":"3f70af768fdf9e47","entry":"TrueRanker","repo":"BorealisAI/ranksim-imbalanced-regression","repo_kind":"official","path":"agedb-dir/ranksim.py","file_url":"https://github.com/BorealisAI/ranksim-imbalanced-regression/blob/HEAD/agedb-dir/ranksim.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"3f70af768fdf9e47"}},{"code_sha256_prefix":"2e1e2e8c543195f7","entry":"rank","repo":"BorealisAI/ranksim-imbalanced-regression","repo_kind":"official","path":"agedb-dir/ranksim.py","file_url":"https://github.com/BorealisAI/ranksim-imbalanced-regression/blob/HEAD/agedb-dir/ranksim.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"2e1e2e8c543195f7"}},{"code_sha256_prefix":"ed49cae4f66fd45c","entry":"rank_normalised","repo":"BorealisAI/ranksim-imbalanced-regression","repo_kind":"official","path":"agedb-dir/ranksim.py","file_url":"https://github.com/BorealisAI/ranksim-imbalanced-regression/blob/HEAD/agedb-dir/ranksim.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"ed49cae4f66fd45c"}},{"code_sha256_prefix":"32107a375435b234","entry":"batchwise_ranking_regularizer","repo":"BorealisAI/ranksim-imbalanced-regression","repo_kind":"official","path":"agedb-dir/ranksim.py","file_url":"https://github.com/BorealisAI/ranksim-imbalanced-regression/blob/HEAD/agedb-dir/ranksim.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"32107a375435b234"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}