{"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/dist-pu-positive-unlabeled-learning-from-a-1","title":"Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective","arxiv_id":"2212.02801","date":"2022-12-06","proceeding":"CVPR 2022 1","authors":["Yunrui Zhao","Qianqian Xu","Yangbangyan Jiang","Peisong Wen","Qingming Huang"],"abstract":"Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, this task is much more challenging due to the absence of any known negative labels. While existing cost-sensitive-based methods have achieved state-of-the-art performances, they explicitly minimize the risk of classifying unlabeled data as negative samples, which might result in a negative-prediction preference of the classifier. To alleviate this issue, we resort to a label distribution perspective for PU learning in this paper. Noticing that the label distribution of unlabeled data is fixed when the class prior is known, it can be naturally used as learning supervision for the model. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions, which is formulated by aligning their expectations. Moreover, we further adopt the entropy minimization and Mixup regularization to avoid the trivial solution of the label distribution consistency on unlabeled data and mitigate the consequent confirmation bias. Experiments on three benchmark datasets validate the effectiveness of the proposed method.Code available at: https://github.com/Ray-rui/Dist-PU-Positive-Unlabeled-Learning-from-a-Label-Distribution-Perspective.","url_abs":"https://arxiv.org/abs/2212.02801v1","url_pdf":"https://arxiv.org/pdf/2212.02801v1.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":"dist-pu-positive-unlabeled-learning-from-a-1","repo_url":"https://github.com/ray-rui/dist-pu-positive-unlabeled-learning-from-a-label-distribution-perspective","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"mixup","method_name":"Mixup"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2212.02801","atlas_url":"https://app.syntology.ai/?focus=2212.02801","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.02801"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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/ray-rui/dist-pu-positive-unlabeled-learning-from-a-label-distribution-perspective","reach":null}],"summary":{"ran":1},"by_repo_kind":{"official":{"samples":1,"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":1,"samples":[{"code_sha256_prefix":"b262263a1c0f0c86","entry":"LabelDistributionLoss","repo":"ray-rui/dist-pu-positive-unlabeled-learning-from-a-label-distribution-perspective","repo_kind":"official","path":"losses/distributionLoss.py","file_url":"https://github.com/ray-rui/dist-pu-positive-unlabeled-learning-from-a-label-distribution-perspective/blob/HEAD/losses/distributionLoss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b262263a1c0f0c86"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}