{"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/class-prior-free-positive-unlabeled-learning","title":"Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery","arxiv_id":"2308.15081","date":"2023-08-29","proceeding":"ICCV 2023 1","authors":["Hengwei Zhao","Xinyu Wang","Jingtao Li","Yanfei Zhong"],"abstract":"Positive-unlabeled learning (PU learning) in hyperspectral remote sensing imagery (HSI) is aimed at learning a binary classifier from positive and unlabeled data, which has broad prospects in various earth vision applications. However, when PU learning meets limited labeled HSI, the unlabeled data may dominate the optimization process, which makes the neural networks overfit the unlabeled data. In this paper, a Taylor variational loss is proposed for HSI PU learning, which reduces the weight of the gradient of the unlabeled data by Taylor series expansion to enable the network to find a balance between overfitting and underfitting. In addition, the self-calibrated optimization strategy is designed to stabilize the training process. Experiments on 7 benchmark datasets (21 tasks in total) validate the effectiveness of the proposed method. Code is at: https://github.com/Hengwei-Zhao96/T-HOneCls.","url_abs":"https://arxiv.org/abs/2308.15081v1","url_pdf":"https://arxiv.org/pdf/2308.15081v1.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":"class-prior-free-positive-unlabeled-learning","repo_url":"https://github.com/hengwei-zhao96/t-honecls","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2308.15081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.15081"}},"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/hengwei-zhao96/t-honecls","reach":{"status":"ok"}},{"provenance":"deterministic:regex_extraction","url":"https://github.com/Hengwei-Zhao96/T-HOneCls","reach":{"status":"ok"}}],"summary":{"ran":2},"by_repo_kind":{"official":{"samples":2,"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":2,"samples":[{"code_sha256_prefix":"5a34044ba2fa3c84","entry":"Registry","repo":"Hengwei-Zhao96/T-HOneCls","repo_kind":"official","path":"HOC/loss_functions/pf_vpu_loss.py","file_url":"https://github.com/Hengwei-Zhao96/T-HOneCls/blob/HEAD/HOC/loss_functions/pf_vpu_loss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"5a34044ba2fa3c84"}},{"code_sha256_prefix":"d9a547dc0a7bb2ef","entry":"TaylorVarPULossPf","repo":"Hengwei-Zhao96/T-HOneCls","repo_kind":"official","path":"HOC/loss_functions/pf_vpu_loss.py","file_url":"https://github.com/Hengwei-Zhao96/T-HOneCls/blob/HEAD/HOC/loss_functions/pf_vpu_loss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"d9a547dc0a7bb2ef"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}