{"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/self-training-for-few-shot-transfer-across-1","title":"Self-training for Few-shot Transfer Across Extreme Task Differences","arxiv_id":"2010.07734","date":"2020-10-15","proceeding":"ICLR 2021 1","authors":["Cheng Perng Phoo","Bharath Hariharan"],"abstract":"Most few-shot learning techniques are pre-trained on a large, labeled \"base dataset\". In problem domains where such large labeled datasets are not available for pre-training (e.g., X-ray, satellite images), one must resort to pre-training in a different \"source\" problem domain (e.g., ImageNet), which can be very different from the desired target task. Traditional few-shot and transfer learning techniques fail in the presence of such extreme differences between the source and target tasks. In this paper, we present a simple and effective solution to tackle this extreme domain gap: self-training a source domain representation on unlabeled data from the target domain. We show that this improves one-shot performance on the target domain by 2.9 points on average on the challenging BSCD-FSL benchmark consisting of datasets from multiple domains. Our code is available at https://github.com/cpphoo/STARTUP.","url_abs":"https://arxiv.org/abs/2010.07734v2","url_pdf":"https://arxiv.org/pdf/2010.07734v2.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":"self-training-for-few-shot-transfer-across-1","repo_url":"https://github.com/cpphoo/STARTUP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2010.07734","atlas_url":"https://app.syntology.ai/?focus=2010.07734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.07734"}},"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":"deterministic:regex_extraction","url":"https://github.com/cpphoo/STARTUP","reach":null}],"summary":{"ran_draft_wrong":2,"ran_honours":1,"ran_fixture":1},"by_repo_kind":{"official":{"samples":4,"ran":4,"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":"40e3c26a53b0401f","entry":"checkpoint","repo":"cpphoo/startup","repo_kind":"official","path":"student_STARTUP/STARTUP.py","file_url":"https://github.com/cpphoo/startup/blob/HEAD/student_STARTUP/STARTUP.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"40e3c26a53b0401f"}},{"code_sha256_prefix":"5f8ebcffdd4581fb","entry":"load_checkpoint","repo":"cpphoo/startup","repo_kind":"official","path":"student_STARTUP/STARTUP.py","file_url":"https://github.com/cpphoo/startup/blob/HEAD/student_STARTUP/STARTUP.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5f8ebcffdd4581fb"}},{"code_sha256_prefix":"abc49ca8b1fc39a0","entry":"pseudolabel_dataset","repo":"cpphoo/startup","repo_kind":"official","path":"student_STARTUP/STARTUP.py","file_url":"https://github.com/cpphoo/startup/blob/HEAD/student_STARTUP/STARTUP.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"abc49ca8b1fc39a0"}},{"code_sha256_prefix":"4a2cdcb2fca9e0de","entry":"pseudolabel_dataset","repo":"cpphoo/STARTUP","repo_kind":"official","path":"student_STARTUP/STARTUP.py","file_url":"https://github.com/cpphoo/STARTUP/blob/HEAD/student_STARTUP/STARTUP.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4a2cdcb2fca9e0de"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}