{"id":"https://openalex.org/W4362710624","doi":"https://doi.org/10.1145/3582649.3582667","title":"Complementary Label Queries for Efficient Active Learning","display_name":"Complementary Label Queries for Efficient Active Learning","publication_year":2023,"publication_date":"2023-01-06","ids":{"openalex":"https://openalex.org/W4362710624","doi":"https://doi.org/10.1145/3582649.3582667"},"language":"en","primary_location":{"id":"doi:10.1145/3582649.3582667","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3582649.3582667","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3582649.3582667","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Image and Graphics Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3582649.3582667","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066002941","display_name":"Shengyuan Liu","orcid":"https://orcid.org/0000-0002-2880-695X"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengyuan Liu","raw_affiliation_strings":["Key Lab of Intelligent Computing Based Big Data of Zhejiang Province, Zhejiang University, China"],"raw_orcid":"https://orcid.org/0000-0002-2880-695X","affiliations":[{"raw_affiliation_string":"Key Lab of Intelligent Computing Based Big Data of Zhejiang Province, Zhejiang University, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035912428","display_name":"Tianlei Hu","orcid":"https://orcid.org/0000-0003-0744-6454"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianlei Hu","raw_affiliation_strings":["Key Lab of Intelligent Computing Based Big Data of Zhejiang Province, Zhejiang University, China"],"raw_orcid":"https://orcid.org/0000-0003-0744-6454","affiliations":[{"raw_affiliation_string":"Key Lab of Intelligent Computing Based Big Data of Zhejiang Province, Zhejiang University, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100451997","display_name":"Ke Chen","orcid":"https://orcid.org/0000-0002-3062-0900"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Chen","raw_affiliation_strings":["Key Lab of Intelligent Computing Based Big Data of Zhejiang Province, Zhejiang University, China"],"raw_orcid":"https://orcid.org/0000-0002-3062-0900","affiliations":[{"raw_affiliation_string":"Key Lab of Intelligent Computing Based Big Data of Zhejiang Province, Zhejiang University, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072624134","display_name":"Yunqing Mao","orcid":"https://orcid.org/0000-0002-6858-9929"},"institutions":[{"id":"https://openalex.org/I4210144487","display_name":"Cloud Computing Center","ror":"https://ror.org/04aa0zm65","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210144487"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunqing Mao","raw_affiliation_strings":["City Cloud Science and Technology (China)Co., Ltd., China"],"raw_orcid":"https://orcid.org/0000-0002-6858-9929","affiliations":[{"raw_affiliation_string":"City Cloud Science and Technology (China)Co., Ltd., China","institution_ids":["https://openalex.org/I4210144487"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4346,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.54986047,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9883999824523926,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9796000123023987,"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":0.8413569927215576},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6171253323554993},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6079809665679932},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.5866153240203857},{"id":"https://openalex.org/keywords/ask-price","display_name":"Ask price","score":0.580001950263977},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5421882271766663},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.49936985969543457},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4503539502620697},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4173932373523712}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8413569927215576},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6171253323554993},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6079809665679932},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.5866153240203857},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.580001950263977},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5421882271766663},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.49936985969543457},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4503539502620697},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4173932373523712},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C136264566","wikidata":"https://www.wikidata.org/wiki/Q159810","display_name":"Economy","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3582649.3582667","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3582649.3582667","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3582649.3582667","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Image and Graphics Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3582649.3582667","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3582649.3582667","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3582649.3582667","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Image and Graphics Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4362710624.pdf","grobid_xml":"https://content.openalex.org/works/W4362710624.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1580375566","https://openalex.org/W2112796928","https://openalex.org/W2117763124","https://openalex.org/W2171671120","https://openalex.org/W2194775991","https://openalex.org/W2798820905","https://openalex.org/W2808575719","https://openalex.org/W2896803982","https://openalex.org/W2903158431","https://openalex.org/W2911583416","https://openalex.org/W2963909971","https://openalex.org/W2964782060","https://openalex.org/W3005603580","https://openalex.org/W3095830519","https://openalex.org/W3118608800","https://openalex.org/W6679716015","https://openalex.org/W6680836039","https://openalex.org/W6686604339","https://openalex.org/W6729147614","https://openalex.org/W6735374517","https://openalex.org/W6738532226","https://openalex.org/W6742900778","https://openalex.org/W6748682970","https://openalex.org/W6756615331","https://openalex.org/W6761381025","https://openalex.org/W6762256473","https://openalex.org/W6772389607","https://openalex.org/W6782595193"],"related_works":["https://openalex.org/W2949671220","https://openalex.org/W4317548404","https://openalex.org/W3022007134","https://openalex.org/W2130553454","https://openalex.org/W2033364610","https://openalex.org/W2797776314","https://openalex.org/W3163689946","https://openalex.org/W2153927146","https://openalex.org/W3104108945","https://openalex.org/W4390190783"],"abstract_inverted_index":{"Many":[0],"active":[1,44,48],"learning":[2,45,49],"methods":[3],"are":[4,98],"based":[5,139],"on":[6,140,174],"the":[7,15,88,92,104,125,135,141,155,178,183],"assumption":[8],"that":[9,133],"a":[10,42,72,130,160],"learner":[11],"simply":[12],"asks":[13],"for":[14],"true":[16],"labels":[17,52,150],"of":[18,87,137,143,180,185],"some":[19,62],"training":[20,78],"data":[21,75],"from":[22,67,110,148],"annotators.":[23],"Unfortunately,":[24],"it":[25],"is":[26,106,116],"expensive":[27],"to":[28,90,108,118,121,146,163],"exactly":[29],"annotate":[30],"instances":[31,79,120,138],"in":[32,61,102],"real-world":[33],"classification":[34],"tasks.":[35],"To":[36],"mitigate":[37],"this":[38,170],"problem,":[39],"we":[40,128,158],"propose":[41,129,159],"novel":[43,131,171],"setting,":[46],"named":[47],"with":[50,80],"complementary":[51,81,112,152],"(ALCL).":[53],"The":[54],"ALCL":[55,69,181],"learners":[56,70],"only":[57,84],"ask":[58],"yes/no":[59],"questions":[60],"classes.":[63],"After":[64],"receiving":[65],"answers":[66],"annotators,":[68],"get":[71],"few":[73],"supervised":[74],"and":[76,114,151,182],"more":[77],"labels,":[82,113],"which":[83,91],"specify":[85],"one":[86],"classes":[89],"pattern":[93],"does":[94],"not":[95],"belong.":[96],"There":[97],"two":[99],"challenging":[100],"issues":[101],"ALCL:":[103],"first":[105,126],"how":[107,117],"learn":[109,147],"these":[111],"another":[115],"sample":[119],"be":[122],"queried.":[123],"For":[124,154],"issue,":[127,157],"method":[132],"redistributes":[134],"weights":[136],"balance":[142],"category":[144],"contribution":[145],"ordinary":[149],"labels.":[153],"second":[156],"weighting":[161],"mechanism":[162],"improve":[164],"existing":[165],"uncertainty-based":[166],"sampling":[167],"strategies":[168],"under":[169],"setup.":[172],"Experiments":[173],"various":[175],"datasets":[176],"demonstrate":[177],"feasibility":[179],"superiority":[184],"our":[186],"approaches.":[187]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}