{"id":"https://openalex.org/W4308067888","doi":"https://doi.org/10.1109/icip46576.2022.9897554","title":"Exemplar-Free Online Continual Learning","display_name":"Exemplar-Free Online Continual Learning","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308067888","doi":"https://doi.org/10.1109/icip46576.2022.9897554"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897554","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897554","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"article","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/A5063620170","display_name":"Jiangpeng He","orcid":"https://orcid.org/0000-0002-8552-9880"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jiangpeng He","raw_affiliation_strings":["Purdue University,Elmore Family School of Electrical and Computer Engineering,West Lafayette,Indiana,USA,47906"],"affiliations":[{"raw_affiliation_string":"Purdue University,Elmore Family School of Electrical and Computer Engineering,West Lafayette,Indiana,USA,47906","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001380619","display_name":"Fengqing Zhu","orcid":"https://orcid.org/0000-0002-3863-3220"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fengqing Zhu","raw_affiliation_strings":["Purdue University,Elmore Family School of Electrical and Computer Engineering,West Lafayette,Indiana,USA,47906"],"affiliations":[{"raw_affiliation_string":"Purdue University,Elmore Family School of Electrical and Computer Engineering,West Lafayette,Indiana,USA,47906","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5063620170"],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":1.4543,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.84243697,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"541","last_page":"545"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9580000042915344,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9332000017166138,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8362810611724854},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5994725227355957},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5946241021156311},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5636714100837708},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5488933324813843},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.509465754032135},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5001685619354248},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.432269811630249}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8362810611724854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5994725227355957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5946241021156311},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5636714100837708},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5488933324813843},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.509465754032135},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5001685619354248},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.432269811630249},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897554","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897554","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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":"2022 IEEE International Conference on Image Processing (ICIP)","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":34,"referenced_works":["https://openalex.org/W1682403713","https://openalex.org/W1964168965","https://openalex.org/W1997865285","https://openalex.org/W2117539524","https://openalex.org/W2194775991","https://openalex.org/W2473930607","https://openalex.org/W2884282566","https://openalex.org/W2902625698","https://openalex.org/W2947461406","https://openalex.org/W2948734064","https://openalex.org/W2954929116","https://openalex.org/W2964189064","https://openalex.org/W2968596670","https://openalex.org/W2971176100","https://openalex.org/W3034451759","https://openalex.org/W3034856281","https://openalex.org/W3097784654","https://openalex.org/W3118608800","https://openalex.org/W3148706139","https://openalex.org/W3175853876","https://openalex.org/W3217076946","https://openalex.org/W4212807160","https://openalex.org/W4295883599","https://openalex.org/W4298116016","https://openalex.org/W4312478260","https://openalex.org/W6638523607","https://openalex.org/W6741087337","https://openalex.org/W6741217325","https://openalex.org/W6757384668","https://openalex.org/W6763462227","https://openalex.org/W6764645560","https://openalex.org/W6768044712","https://openalex.org/W6787972765","https://openalex.org/W6792947588"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2028665553","https://openalex.org/W4230315250","https://openalex.org/W2086519370","https://openalex.org/W2354233396"],"abstract_inverted_index":{"Targeted":[0],"for":[1,46,80],"real":[2],"world":[3],"scenarios,":[4],"online":[5,117],"continual":[6,69],"learning":[7],"aims":[8],"to":[9,84,165],"learn":[10],"new":[11],"tasks":[12],"from":[13],"sequentially":[14],"available":[15],"data":[16,22,43,112],"under":[17,155],"the":[18,28,49,55,61,102],"condition":[19],"that":[20,141],"each":[21],"is":[23,51,64,105,163],"observed":[24],"only":[25],"once":[26],"by":[27,37,96],"learner.":[29],"Though":[30],"recent":[31],"works":[32],"have":[33],"made":[34],"remarkable":[35],"achievements":[36],"storing":[38,73],"part":[39],"of":[40,57],"learned":[41],"task":[42,126],"as":[44],"exemplars":[45,59,74,159,175],"knowledge":[47],"replay,":[48],"performance":[50,168],"greatly":[52],"relied":[53],"on":[54,110,123,131],"size":[56,173],"stored":[58],"while":[60],"storage":[62],"consumption":[63],"a":[65,92],"significant":[66],"constraint":[67],"in":[68],"learning.":[70],"In":[71,87],"addition,":[72],"may":[75],"not":[76],"always":[77],"be":[78],"feasible":[79],"certain":[81],"applications":[82],"due":[83],"privacy":[85],"concerns.":[86],"this":[88],"work,":[89],"we":[90],"propose":[91],"novel":[93],"exemplar-free":[94],"method":[95,143],"leveraging":[97],"nearest-class-mean":[98],"(NCM)":[99],"classifier":[100],"where":[101],"class":[103],"mean":[104,118],"estimated":[106],"during":[107],"training":[108],"phase":[109],"all":[111],"seen":[113],"so":[114],"far":[115],"through":[116],"update":[119],"criteria.":[120],"We":[121],"focus":[122],"image":[124],"classification":[125],"and":[127,136,162],"conduct":[128],"extensive":[129],"experiments":[130],"benchmark":[132],"datasets":[133],"including":[134],"CIFAR-100":[135],"Food-1k.":[137],"The":[138],"results":[139],"demonstrate":[140],"our":[142],"without":[144],"using":[145],"any":[146],"exemplar":[147,172],"outperforms":[148],"state-of-the-art":[149],"exemplar-based":[150],"approaches":[151],"with":[152,170],"large":[153],"margins":[154],"standard":[156],"protocol":[157],"(20":[158],"per":[160,176],"class)":[161],"able":[164],"achieve":[166],"competitive":[167],"even":[169],"larger":[171],"(100":[174],"class).":[177]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
