{"id":"https://openalex.org/W3205976355","doi":"https://doi.org/10.3390/a15050147","title":"Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning","display_name":"Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning","publication_year":2022,"publication_date":"2022-04-26","ids":{"openalex":"https://openalex.org/W3205976355","doi":"https://doi.org/10.3390/a15050147","mag":"3205976355"},"language":"en","primary_location":{"id":"doi:10.3390/a15050147","doi":"https://doi.org/10.3390/a15050147","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/a15050147","pdf_url":"https://www.mdpi.com/1999-4893/15/5/147/pdf?version=1650974986","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","poland-40","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["arxiv","crossref","datacite","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1999-4893/15/5/147/pdf?version=1650974986","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101546279","display_name":"Yuqing Hu","orcid":"https://orcid.org/0000-0002-9093-1356","observed_orcids":["https://orcid.org/0000-0002-9093-1356"]},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I19370010","display_name":"Orange (France)","ror":"https://ror.org/035j0tq82","country_code":"FR","type":"company","lineage":["https://openalex.org/I19370010"]},{"id":"https://openalex.org/I4210123702","display_name":"Laboratoire des Sciences et Techniques de l\u2019Information de la Communication et de la Connaissance","ror":"https://ror.org/0266kfd37","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I180375564","https://openalex.org/I201181511","https://openalex.org/I205703379","https://openalex.org/I2802204017","https://openalex.org/I4210123702","https://openalex.org/I4210127572","https://openalex.org/I4210145102","https://openalex.org/I4210148559","https://openalex.org/I4210159245","https://openalex.org/I4405260085"]},{"id":"https://openalex.org/I4210127572","display_name":"IMT Atlantique","ror":"https://ror.org/030hj3061","country_code":"FR","type":"education","lineage":["https://openalex.org/I205703379","https://openalex.org/I4210127572"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Yuqing Hu","raw_affiliation_strings":["IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238 Brest, France","Orange S.A., F-84100 Paris, France"],"raw_orcid":"https://orcid.org/0000-0002-9093-1356","affiliations":[{"raw_affiliation_string":"IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238 Brest, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210123702","https://openalex.org/I4210127572"]},{"raw_affiliation_string":"Orange S.A., F-84100 Paris, France","institution_ids":["https://openalex.org/I19370010"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019140379","display_name":"St\u00e9phane Pateux","orcid":"https://orcid.org/0000-0002-4345-7882","observed_orcids":["https://orcid.org/0000-0002-4345-7882"]},"institutions":[{"id":"https://openalex.org/I19370010","display_name":"Orange (France)","ror":"https://ror.org/035j0tq82","country_code":"FR","type":"company","lineage":["https://openalex.org/I19370010"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"St\u00e9phane Pateux","raw_affiliation_strings":["Orange S.A., F-84100 Paris, France"],"raw_orcid":"https://orcid.org/0000-0002-4345-7882","affiliations":[{"raw_affiliation_string":"Orange S.A., F-84100 Paris, France","institution_ids":["https://openalex.org/I19370010"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068330560","display_name":"Vincent Gripon","orcid":"https://orcid.org/0000-0002-4353-4542","observed_orcids":["https://orcid.org/0000-0002-4353-4542"]},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210123702","display_name":"Laboratoire des Sciences et Techniques de l\u2019Information de la Communication et de la Connaissance","ror":"https://ror.org/0266kfd37","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I180375564","https://openalex.org/I201181511","https://openalex.org/I205703379","https://openalex.org/I2802204017","https://openalex.org/I4210123702","https://openalex.org/I4210127572","https://openalex.org/I4210145102","https://openalex.org/I4210148559","https://openalex.org/I4210159245","https://openalex.org/I4405260085"]},{"id":"https://openalex.org/I4210127572","display_name":"IMT Atlantique","ror":"https://ror.org/030hj3061","country_code":"FR","type":"education","lineage":["https://openalex.org/I205703379","https://openalex.org/I4210127572"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Vincent Gripon","raw_affiliation_strings":["IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238 Brest, France","Lab-STICC_2AI - Equipe Algorithm Architecture Interactions (France)"],"raw_orcid":"https://orcid.org/0000-0002-4353-4542","affiliations":[{"raw_affiliation_string":"IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238 Brest, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210123702","https://openalex.org/I4210127572"]},{"raw_affiliation_string":"Lab-STICC_2AI - Equipe Algorithm Architecture Interactions (France)","institution_ids":["https://openalex.org/I4210123702"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1400,"currency":"CHF","value_usd":1466},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1466},"fwci":2.5801,"has_fulltext":true,"cited_by_count":39,"citation_normalized_percentile":{"value":0.91189364,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"15","issue":"5","first_page":"147","last_page":"147"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":1.0,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":1.0,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.0,"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/few-shot-learning","display_name":"few-shot learning","score":0.9779999852180481},{"id":"https://openalex.org/keywords/few-shot-classification","display_name":"few-shot classification","score":0.9459999799728394},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.6579999923706055},{"id":"https://openalex.org/keywords/inductive-learning","display_name":"inductive learning","score":0.5580000281333923},{"id":"https://openalex.org/keywords/transductive-learning","display_name":"transductive learning","score":0.996999979019165},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.5509999990463257}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7513307332992554},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.733569860458374},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6955551505088806},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6767417192459106},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6209536790847778},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5467336773872375},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.5442672967910767},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5252425670623779},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44161704182624817},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4351191520690918},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.41911154985427856},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.411540150642395},{"id":"https://openalex.org/C3019835501","wikidata":"https://www.wikidata.org/wiki/Q1310130","display_name":"Single shot","level":2,"score":0.410122811794281},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1902199387550354},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.17206883430480957},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12523427605628967},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/a15050147","doi":"https://doi.org/10.3390/a15050147","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/a15050147","pdf_url":"https://www.mdpi.com/1999-4893/15/5/147/pdf?version=1650974986","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","poland-40","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2110.09446","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.09446","pdf_url":"https://arxiv.org/pdf/2110.09446","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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:HAL:hal-03675145v1","doi":null,"is_oa":true,"landing_page_url":"https://hal.science/hal-03675145","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4405252976","host_organization_name":"Centre pour la Communication Scientifique Directe","host_organization_lineage":["https://openalex.org/I4405252976"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Algorithms, 2022, 15 (5), pp.147. ⟨10.3390/a15050147⟩","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:192c67596d544736b0d7edb1908a24a1","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/192c67596d544736b0d7edb1908a24a1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Algorithms, Vol 15, Iss 5, p 147 (2022)","raw_type":"article"},{"id":"doi:10.48550/arxiv.2110.09446","doi":"https://doi.org/10.48550/arxiv.2110.09446","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2110.09446","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.3390/a15050147","doi":"https://doi.org/10.3390/a15050147","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/a15050147","pdf_url":"https://www.mdpi.com/1999-4893/15/5/147/pdf?version=1650974986","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","poland-40","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3205976355.pdf","grobid_xml":"https://content.openalex.org/works/W3205976355.grobid-xml"},"referenced_works_count":65,"referenced_works":["https://openalex.org/W385466589","https://openalex.org/W1499991161","https://openalex.org/W1836465849","https://openalex.org/W1983320747","https://openalex.org/W2009172320","https://openalex.org/W2049633694","https://openalex.org/W2091406001","https://openalex.org/W2131036894","https://openalex.org/W2158131535","https://openalex.org/W2192643373","https://openalex.org/W2194775991","https://openalex.org/W2248269543","https://openalex.org/W2272331516","https://openalex.org/W2319794630","https://openalex.org/W2432717477","https://openalex.org/W2601450892","https://openalex.org/W2604763608","https://openalex.org/W2742093937","https://openalex.org/W2753160622","https://openalex.org/W2763323349","https://openalex.org/W2766270016","https://openalex.org/W2792643794","https://openalex.org/W2803895400","https://openalex.org/W2884901161","https://openalex.org/W2895671740","https://openalex.org/W2895899670","https://openalex.org/W2909383239","https://openalex.org/W2911906319","https://openalex.org/W2921861056","https://openalex.org/W2963350370","https://openalex.org/W2963446712","https://openalex.org/W2965535361","https://openalex.org/W2973006577","https://openalex.org/W2986604550","https://openalex.org/W2987878148","https://openalex.org/W2995322030","https://openalex.org/W2995360785","https://openalex.org/W3007881937","https://openalex.org/W3010597097","https://openalex.org/W3012080662","https://openalex.org/W3012195000","https://openalex.org/W3012255272","https://openalex.org/W3034312118","https://openalex.org/W3035143213","https://openalex.org/W3035302051","https://openalex.org/W3035654071","https://openalex.org/W3081382462","https://openalex.org/W3086441688","https://openalex.org/W3097337894","https://openalex.org/W3102109197","https://openalex.org/W3108878460","https://openalex.org/W3108975329","https://openalex.org/W3122520870","https://openalex.org/W3127517787","https://openalex.org/W3128270024","https://openalex.org/W3135385999","https://openalex.org/W3160262112","https://openalex.org/W3160404276","https://openalex.org/W3175320807","https://openalex.org/W3188064562","https://openalex.org/W3193823652","https://openalex.org/W3204699193","https://openalex.org/W3216552527","https://openalex.org/W4255421341","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W3204699193","https://openalex.org/W4408029108","https://openalex.org/W4297636008","https://openalex.org/W4402351155","https://openalex.org/W3212897593","https://openalex.org/W7169727220","https://openalex.org/W7213458319","https://openalex.org/W4313014998","https://openalex.org/W4319075182","https://openalex.org/W4406682119"],"abstract_inverted_index":{"In":[0,38,113],"many":[1,43,78],"real-life":[2],"problems,":[3],"it":[4,81],"is":[5,24,62,82,96,148,158,175],"difficult":[6],"to":[7,29,84,150],"acquire":[8],"or":[9],"label":[10],"large":[11],"amounts":[12],"of":[13,52,88,93,137],"data,":[14],"resulting":[15],"in":[16,134],"so-called":[17],"few-shot":[18,22,89,138],"learning":[19],"problems.":[20],"However,":[21],"classification":[23],"a":[25,57,67,118,123,171],"challenging":[26],"problem":[27],"due":[28],"the":[30,39,49,74,86,103,108,135],"uncertainty":[31],"caused":[32],"by":[33,65],"using":[34,66],"few":[35,41],"labeled":[36,79],"samples.":[37],"past":[40],"years,":[42],"methods":[44,95],"have":[45],"been":[46],"proposed":[47],"with":[48,122,152],"common":[50],"aim":[51],"transferring":[53],"knowledge":[54],"acquired":[55],"on":[56,131,160],"previously":[58],"solved":[59],"task,":[60],"which":[61],"often":[63,99],"achieved":[64],"pretrained":[68],"feature":[69],"extractor.":[70],"As":[71],"such,":[72],"if":[73],"initial":[75],"task":[76],"contains":[77],"samples,":[80],"possible":[83],"circumvent":[85],"limitations":[87],"learning.":[90],"A":[91],"shortcoming":[92],"existing":[94],"that":[97],"they":[98],"require":[100],"priors":[101],"about":[102],"data":[104],"distribution,":[105],"such":[106,143],"as":[107,129],"balance":[109],"between":[110],"considered":[111],"classes.":[112],"this":[114],"paper,":[115],"we":[116],"propose":[117],"novel":[119],"transfer-based":[120],"method":[121],"double":[124],"aim:":[125],"providing":[126],"state-of-the-art":[127],"performance,":[128],"reported":[130],"standardized":[132],"datasets":[133],"field":[136],"learning,":[139],"while":[140],"not":[141],"requiring":[142],"restrictive":[144],"priors.":[145],"Our":[146],"methodology":[147],"able":[149],"cope":[151],"both":[153],"inductive":[154],"cases,":[155,169],"where":[156,170],"prediction":[157,174],"performed":[159],"test":[161],"samples":[162],"independently":[163],"from":[164],"each":[165],"other,":[166],"and":[167],"transductive":[168],"joint":[172],"(batch)":[173],"performed.":[176]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":9}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2021-10-25T00:00:00"}