{"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/multidimensional-belief-quantification-for","title":"Multidimensional Belief Quantification for Label-Efficient Meta-Learning","arxiv_id":"2203.12768","date":"2022-03-23","proceeding":"CVPR 2022 1","authors":["Deep Pandey","Qi Yu"],"abstract":"Optimization-based meta-learning offers a promising direction for few-shot learning that is essential for many real-world computer vision applications. However, learning from few samples introduces uncertainty, and quantifying model confidence for few-shot predictions is essential for many critical domains. Furthermore, few-shot tasks used in meta training are usually sampled randomly from a task distribution for an iterative model update, leading to high labeling costs and computational overhead in meta-training. We propose a novel uncertainty-aware task selection model for label efficient meta-learning. The proposed model formulates a multidimensional belief measure, which can quantify the known uncertainty and lower bound the unknown uncertainty of any given task. Our theoretical result establishes an important relationship between the conflicting belief and the incorrect belief. The theoretical result allows us to estimate the total uncertainty of a task, which provides a principled criterion for task selection. A novel multi-query task formulation is further developed to improve both the computational and labeling efficiency of meta-learning. Experiments conducted over multiple real-world few-shot image classification tasks demonstrate the effectiveness of the proposed model.","url_abs":"https://arxiv.org/abs/2203.12768v1","url_pdf":"https://arxiv.org/pdf/2203.12768v1.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":"multidimensional-belief-quantification-for","repo_url":"https://github.com/pandeydeep9/units-ml-cvpr-22","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.12768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12768"}},"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/pandeydeep9/units-ml-cvpr-22","reach":null}],"summary":{"ran":5,"ran_draft_wrong":1,"unverified":3},"by_repo_kind":{"official":{"samples":9,"ran":6,"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":9,"samples":[{"code_sha256_prefix":"5493b739ad36ab86","entry":"LSLRGradientDescentLearningRule","repo":"pandeydeep9/units-ml-cvpr-22","repo_kind":"official","path":"few_shot_learning_system_units.py","file_url":"https://github.com/pandeydeep9/units-ml-cvpr-22/blob/HEAD/few_shot_learning_system_units.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":"5493b739ad36ab86"}},{"code_sha256_prefix":"9f46766fec630647","entry":"MetaBatchNormLayer","repo":"pandeydeep9/units-ml-cvpr-22","repo_kind":"official","path":"few_shot_learning_system_units.py","file_url":"https://github.com/pandeydeep9/units-ml-cvpr-22/blob/HEAD/few_shot_learning_system_units.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":"9f46766fec630647"}},{"code_sha256_prefix":"68b01d61198c535c","entry":"MetaConv2dLayer","repo":"pandeydeep9/units-ml-cvpr-22","repo_kind":"official","path":"few_shot_learning_system_units.py","file_url":"https://github.com/pandeydeep9/units-ml-cvpr-22/blob/HEAD/few_shot_learning_system_units.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"68b01d61198c535c"}},{"code_sha256_prefix":"f5b1653868fba440","entry":"MetaLayerNormLayer","repo":"pandeydeep9/units-ml-cvpr-22","repo_kind":"official","path":"few_shot_learning_system_units.py","file_url":"https://github.com/pandeydeep9/units-ml-cvpr-22/blob/HEAD/few_shot_learning_system_units.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":"f5b1653868fba440"}},{"code_sha256_prefix":"08134674aed51b60","entry":"MetaLinearLayer","repo":"pandeydeep9/units-ml-cvpr-22","repo_kind":"official","path":"few_shot_learning_system_units.py","file_url":"https://github.com/pandeydeep9/units-ml-cvpr-22/blob/HEAD/few_shot_learning_system_units.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"08134674aed51b60"}},{"code_sha256_prefix":"94cd47c72a983ad8","entry":"extract_top_level_dict","repo":"pandeydeep9/units-ml-cvpr-22","repo_kind":"official","path":"few_shot_learning_system_units.py","file_url":"https://github.com/pandeydeep9/units-ml-cvpr-22/blob/HEAD/few_shot_learning_system_units.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":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"94cd47c72a983ad8"}},{"code_sha256_prefix":"8747365a59d77624","entry":"MAMLFewShotClassifier","repo":"pandeydeep9/units-ml-cvpr-22","repo_kind":"official","path":"few_shot_learning_system_units.py","file_url":"https://github.com/pandeydeep9/units-ml-cvpr-22/blob/HEAD/few_shot_learning_system_units.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8747365a59d77624"}},{"code_sha256_prefix":"1d372c13e14ff912","entry":"MetaConvNormLayerReLU","repo":"pandeydeep9/units-ml-cvpr-22","repo_kind":"official","path":"few_shot_learning_system_units.py","file_url":"https://github.com/pandeydeep9/units-ml-cvpr-22/blob/HEAD/few_shot_learning_system_units.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1d372c13e14ff912"}},{"code_sha256_prefix":"bf713b7a50888934","entry":"VGGReLUNormNetwork","repo":"pandeydeep9/units-ml-cvpr-22","repo_kind":"official","path":"few_shot_learning_system_units.py","file_url":"https://github.com/pandeydeep9/units-ml-cvpr-22/blob/HEAD/few_shot_learning_system_units.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"bf713b7a50888934"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}