{"id":"https://openalex.org/W4417490996","doi":"https://doi.org/10.1109/iccv51701.2025.00385","title":"To Label or Not to Label: PALM - a Predictive Model for Evaluating Sample Efficiency in Active Learning Models","display_name":"To Label or Not to Label: PALM - a Predictive Model for Evaluating Sample Efficiency in Active Learning Models","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4417490996","doi":"https://doi.org/10.1109/iccv51701.2025.00385"},"language":"en","primary_location":{"id":"doi:10.1109/iccv51701.2025.00385","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00385","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2507.15381","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106066884","display_name":"Julia Machnio","orcid":null},"institutions":[{"id":"https://openalex.org/I139025015","display_name":"Pioneer (United States)","ror":"https://ror.org/05jke7315","country_code":"US","type":"company","lineage":["https://openalex.org/I139025015","https://openalex.org/I4210104019"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Julia Machnio","raw_affiliation_strings":["Pioneer Centre for AI, University of Copenhagen"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pioneer Centre for AI, University of Copenhagen","institution_ids":["https://openalex.org/I139025015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055800699","display_name":"Mads Nielsen","orcid":"https://orcid.org/0000-0003-1535-068X"},"institutions":[{"id":"https://openalex.org/I139025015","display_name":"Pioneer (United States)","ror":"https://ror.org/05jke7315","country_code":"US","type":"company","lineage":["https://openalex.org/I139025015","https://openalex.org/I4210104019"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mads Nielsen","raw_affiliation_strings":["Pioneer Centre for AI, University of Copenhagen"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pioneer Centre for AI, University of Copenhagen","institution_ids":["https://openalex.org/I139025015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076278565","display_name":"Mostafa Mehdipour Ghazi","orcid":"https://orcid.org/0000-0002-8473-281X"},"institutions":[{"id":"https://openalex.org/I139025015","display_name":"Pioneer (United States)","ror":"https://ror.org/05jke7315","country_code":"US","type":"company","lineage":["https://openalex.org/I139025015","https://openalex.org/I4210104019"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mostafa Mehdipour Ghazi","raw_affiliation_strings":["Pioneer Centre for AI, University of Copenhagen"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pioneer Centre for AI, University of Copenhagen","institution_ids":["https://openalex.org/I139025015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19463767,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4039","last_page":"4048"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9843000173568726,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9843000173568726,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.004600000102072954,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0017000000225380063,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/palm","display_name":"Palm","score":0.6607000231742859},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.5971999764442444},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.583899974822998},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4235000014305115},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.41589999198913574},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4117000102996826},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.38920000195503235},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.38659998774528503}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7301999926567078},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6693000197410583},{"id":"https://openalex.org/C94598645","wikidata":"https://www.wikidata.org/wiki/Q2347874","display_name":"Palm","level":2,"score":0.6607000231742859},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6345999836921692},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.5971999764442444},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.583899974822998},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4235000014305115},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.41589999198913574},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4117000102996826},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.38659998774528503},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.37610000371932983},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34549999237060547},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C34585555","wikidata":"https://www.wikidata.org/wiki/Q1368723","display_name":"Learning curve","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.3118000030517578},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2782000005245209}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/iccv51701.2025.00385","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00385","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2507.15381","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.15381","pdf_url":"https://arxiv.org/pdf/2507.15381","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/6cf9868f-787d-4cce-a7ec-b59f28af5747","is_oa":true,"landing_page_url":"https://researchprofiles.ku.dk/da/publications/6cf9868f-787d-4cce-a7ec-b59f28af5747","pdf_url":"https://curis.ku.dk/ws/files/537429211/2507.15381v1.pdf","source":{"id":"https://openalex.org/S4306401983","display_name":"Research at the University of Copenhagen (University of Copenhagen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I124055696","host_organization_name":"University of Copenhagen","host_organization_lineage":["https://openalex.org/I124055696"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machnio , J , Nielsen , M & Ghazi , M M 2025 ' To Label or Not to Label : PALM -- A Predictive Model for Evaluating Sample Efficiency in Active Learning Models ' .","raw_type":"workingPaper"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2507.15381","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.15381","pdf_url":"https://arxiv.org/pdf/2507.15381","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320943","display_name":"Danmarks Grundforskningsfond","ror":"https://ror.org/00znyv691"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Active":[0,53],"learning":[1,41,137,149],"(AL)":[2],"seeks":[3],"to":[4,34],"reduce":[5],"annotation":[6],"costs":[7],"by":[8],"selecting":[9],"the":[10,36,40,90,155,162,176],"most":[11],"informative":[12],"samples":[13],"for":[14,178],"labeling,":[15],"making":[16],"it":[17],"particularly":[18],"valuable":[19],"in":[20,187],"resource-constrained":[21],"settings.":[22],"However,":[23],"traditional":[24],"evaluation":[25,184],"methods,":[26],"which":[27],"focus":[28],"solely":[29],"on":[30,108],"final":[31],"accuracy,":[32,71],"fail":[33],"capture":[35],"full":[37,136],"dynamics":[38],"of":[39,52,83,92,116,157,164,185],"process.":[42],"To":[43],"address":[44],"this":[45],"gap,":[46],"we":[47],"propose":[48],"PALM":[49,78,104,126,144,174],"(Performance":[50],"Analysis":[51],"Learning":[54],"Models),":[55],"a":[56,80,113],"unified":[57],"and":[58,76,95,110,119,132,154,167,182,190],"interpretable":[59],"mathematical":[60],"model":[61],"that":[62,125],"characterizes":[63],"AL":[64,84,117,158,186],"trajectories":[65],"through":[66,105],"four":[67],"key":[68],"parameters:":[69],"achievable":[70],"coverage":[72],"efficiency,":[73,150],"early-stage":[74],"performance,":[75],"scalability.":[77],"provides":[79],"predictive":[81],"description":[82],"behavior":[85],"from":[86,139],"partial":[87],"observations,":[88],"enabling":[89,161],"estimation":[91],"future":[93],"performance":[94,169],"facilitating":[96],"principled":[97],"comparisons":[98],"across":[99,129],"different":[100],"strategies.":[101],"We":[102],"validate":[103],"extensive":[106],"experiments":[107],"CIFAR-10/100":[109],"ImageNet-50/100/200,":[111],"covering":[112],"wide":[114],"range":[115],"methods":[118],"self-supervised":[120],"embeddings.":[121],"Our":[122],"results":[123],"demonstrate":[124],"generalizes":[127],"effectively":[128],"datasets,":[130],"budgets,":[131],"strategies,":[133],"accurately":[134],"predicting":[135,168],"curves":[138],"limited":[140],"labeled":[141],"data.":[142],"Importantly,":[143],"reveals":[145],"crucial":[146],"insights":[147],"into":[148],"data":[151],"space":[152],"coverage,":[153],"scalability":[156],"methods.":[159],"By":[160],"selection":[163],"cost-effective":[165],"strategies":[166],"under":[170],"tight":[171],"budget":[172],"constraints,":[173],"lays":[175],"basis":[177],"more":[179],"systematic,":[180],"reproducible,":[181],"data-efficient":[183],"both":[188],"research":[189],"real-world":[191],"applications.":[192],"The":[193],"code":[194],"is":[195],"available":[196],"at:":[197],"https://github.com/juliamachnio/PALM.":[198]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}