{"id":"https://openalex.org/W4290943605","doi":"https://doi.org/10.1145/3534678.3539262","title":"Active Model Adaptation Under Unknown Shift","display_name":"Active Model Adaptation Under Unknown Shift","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290943605","doi":"https://doi.org/10.1145/3534678.3539262"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539262","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539262","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087294333","display_name":"Jie-Jing Shao","orcid":"https://orcid.org/0000-0001-8107-114X"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie-Jing Shao","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054244337","display_name":"Yunlu Xu","orcid":"https://orcid.org/0000-0002-2057-6010"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yunlu Xu","raw_affiliation_strings":["Hikvision Research Institute, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hikvision Research Institute, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011185036","display_name":"Zhanzhan Cheng","orcid":"https://orcid.org/0000-0002-5732-1513"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhanzhan Cheng","raw_affiliation_strings":["Hikvision Research Institute, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hikvision Research Institute, Hangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100355152","display_name":"Yu-Feng Li","orcid":"https://orcid.org/0000-0002-7727-4304"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu-Feng Li","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1558","last_page":"1566"},"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":0.9994000196456909,"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":0.9994000196456909,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9955999851226807,"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":0.9947999715805054,"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/computer-science","display_name":"Computer science","score":0.743961751461029},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.7049180865287781},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.7001417279243469},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5778055191040039},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5129315853118896},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.48005446791648865},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4799678325653076},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.46734127402305603},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4643317759037018},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.4269248843193054},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4135757088661194},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14026695489883423}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.743961751461029},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7049180865287781},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.7001417279243469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5778055191040039},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5129315853118896},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.48005446791648865},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4799678325653076},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.46734127402305603},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4643317759037018},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.4269248843193054},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4135757088661194},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14026695489883423},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539262","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539262","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2593768305","https://openalex.org/W2627183927","https://openalex.org/W2962687275","https://openalex.org/W2962708723","https://openalex.org/W2963978428","https://openalex.org/W3174493137","https://openalex.org/W3182214289","https://openalex.org/W3202165894"],"related_works":["https://openalex.org/W2997567050","https://openalex.org/W1630076647","https://openalex.org/W2371138613","https://openalex.org/W2080152487","https://openalex.org/W2048963458","https://openalex.org/W43109613","https://openalex.org/W2359952343","https://openalex.org/W2922249299","https://openalex.org/W2239445980","https://openalex.org/W2120455979"],"abstract_inverted_index":{"Successful":[0],"machine":[1],"learning":[2],"typically":[3],"relies":[4],"on":[5,124,199],"fixed":[6],"data":[7,203],"distribution.":[8],"However,":[9],"due":[10,37],"to":[11,38,45,114,155,171],"unforeseen":[12],"situations":[13],"in":[14,22,26,40,74],"the":[15,27,47,52,109,125,157,163,173,176,182,185,206,215],"open":[16],"world,":[17],"distribution":[18,50,97,140,152],"shift":[19,34],"often":[20,59],"occurs":[21],"applications.":[23],"For":[24],"instance,":[25],"image":[28],"recognition":[29],"task,":[30],"an":[31],"unpredictable":[32],"distributional":[33,158],"may":[35],"occur":[36],"changes":[39],"background":[41],"or":[42],"lighting.":[43],"Furthermore,":[44],"alleviate":[46],"harm":[48],"of":[49,92,135,175,194],"shift,":[51],"resource":[53],"budget":[54],"is":[55],"not":[56],"infinite":[57],"and":[58,84,144,205],"constrained.":[60],"To":[61,161],"cope":[62],"with":[63,116],"such":[64],"a":[65,89,118,130,150,168],"novel":[66],"problem":[67],"Resource":[68],"Constrained":[69],"Adaptation":[70],"under":[71],"Unknown":[72],"Shift,":[73],"this":[75],"paper":[76],"we":[77,87,101,128,148,166,180],"study":[78],"active":[79,93,104,197],"model":[80,94,105,145,183],"adaptation":[81,95,106],"both":[82],"theoretically":[83],"empirically.":[85],"First,":[86],"present":[88,129],"generalization":[90,110],"analysis":[91],"for":[96],"shift.":[98],"In":[99],"theory,":[100],"show":[102,208],"that":[103,138,209],"could":[107,211],"improve":[108],"error":[111],"from":[112],"O(1/N)":[113],"O(1/N),":[115],"only":[117],"few":[119],"queried":[120,187],"samples.":[121,160,178],"Second,":[122],"based":[123],"theoretical":[126],"analysis,":[127],"systemic":[131],"solution":[132],"Auto,":[133],"consisting":[134],"three":[136],"sub-steps,":[137],"is,":[139],"tracking,":[141],"sample":[142],"selection":[143],"adaptation.":[146],"Specifically,":[147],"design":[149],"shifted":[151,159],"detection":[153],"module":[154],"locate":[156],"fit":[162],"labeling":[164],"budget,":[165],"employ":[167],"core-set":[169],"algorithm":[170],"enhance":[172],"informativeness":[174],"selected":[177],"Finally,":[179],"update":[181],"through":[184],"newly":[186],"labeled":[188],"data.":[189],"We":[190],"conduct":[191],"empirical":[192],"studies":[193],"nine":[195],"existing":[196],"strategies":[198],"diverse":[200],"real":[201],"world":[202],"sets":[204],"results":[207],"Auto":[210],"remarkably":[212],"outperform":[213],"all":[214],"baselines.":[216]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}