{"id":"https://openalex.org/W4312301875","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892266","title":"Self-distilled Knowledge Delegator for Exemplar-free Class Incremental Learning","display_name":"Self-distilled Knowledge Delegator for Exemplar-free Class Incremental Learning","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312301875","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892266"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892266","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892266","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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 International Joint Conference on Neural Networks (IJCNN)","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/A5054321716","display_name":"Fanfan Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fanfan Ye","raw_affiliation_strings":["Hikvision Research Institute"],"affiliations":[{"raw_affiliation_string":"Hikvision Research Institute","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035843722","display_name":"Liang Ma","orcid":"https://orcid.org/0000-0003-4228-258X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang Ma","raw_affiliation_strings":["Hikvision Research Institute"],"affiliations":[{"raw_affiliation_string":"Hikvision Research Institute","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047098807","display_name":"Qiaoyong Zhong","orcid":"https://orcid.org/0000-0002-5944-1818"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiaoyong Zhong","raw_affiliation_strings":["Hikvision Research Institute"],"affiliations":[{"raw_affiliation_string":"Hikvision Research Institute","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100636142","display_name":"Di Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Di Xie","raw_affiliation_strings":["Hikvision Research Institute"],"affiliations":[{"raw_affiliation_string":"Hikvision Research Institute","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085955762","display_name":"Shiliang Pu","orcid":"https://orcid.org/0000-0001-5269-7821"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shiliang Pu","raw_affiliation_strings":["Hikvision Research Institute"],"affiliations":[{"raw_affiliation_string":"Hikvision Research Institute","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5054321716"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4158,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.58968972,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.9997000098228455,"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":0.9997000098228455,"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":0.9835000038146973,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8871867656707764},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.7620898485183716},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6518188118934631},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6322650909423828},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6008227467536926},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5657727718353271},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5375219583511353}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8871867656707764},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.7620898485183716},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6518188118934631},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6322650909423828},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6008227467536926},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5657727718353271},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5375219583511353},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892266","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892266","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W1821462560","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2125389028","https://openalex.org/W2155904486","https://openalex.org/W2187089797","https://openalex.org/W2473930607","https://openalex.org/W2533598788","https://openalex.org/W2560647685","https://openalex.org/W2771964490","https://openalex.org/W2791091755","https://openalex.org/W2884282566","https://openalex.org/W2899771611","https://openalex.org/W2948734064","https://openalex.org/W2954929116","https://openalex.org/W2963072899","https://openalex.org/W2963540014","https://openalex.org/W2963559848","https://openalex.org/W2963588172","https://openalex.org/W2963813679","https://openalex.org/W2964067969","https://openalex.org/W2964189064","https://openalex.org/W2978098801","https://openalex.org/W2981751377","https://openalex.org/W2982802130","https://openalex.org/W2995139074","https://openalex.org/W2996610304","https://openalex.org/W2996834813","https://openalex.org/W3002497460","https://openalex.org/W3007041883","https://openalex.org/W3034619766","https://openalex.org/W3034957837","https://openalex.org/W3034997257","https://openalex.org/W3035460915","https://openalex.org/W3035501943","https://openalex.org/W3035965352","https://openalex.org/W3046253174","https://openalex.org/W3099878876","https://openalex.org/W3100156920","https://openalex.org/W3105913416","https://openalex.org/W3118608800","https://openalex.org/W3177306522","https://openalex.org/W4226053218","https://openalex.org/W4287682940","https://openalex.org/W4288348717","https://openalex.org/W4289549433","https://openalex.org/W4301163820","https://openalex.org/W6604653223","https://openalex.org/W6638523607","https://openalex.org/W6677258307","https://openalex.org/W6678815747","https://openalex.org/W6738602802","https://openalex.org/W6741217325","https://openalex.org/W6742852309","https://openalex.org/W6746266179","https://openalex.org/W6746643698","https://openalex.org/W6747837551","https://openalex.org/W6753554323","https://openalex.org/W6755215958","https://openalex.org/W6756040250","https://openalex.org/W6762640628","https://openalex.org/W6772674396","https://openalex.org/W6773622612","https://openalex.org/W6776349011","https://openalex.org/W6782613959","https://openalex.org/W6810411895"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W4289718052","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"Exemplar-free":[0],"incremental":[1,104,108,124],"learning":[2,109],"is":[3,47,73,90],"extremely":[4],"challenging":[5],"due":[6],"to":[7,19,30,56,93,137],"inaccessibility":[8],"of":[9,49,97],"data":[10],"from":[11,52],"old":[12,101],"tasks.":[13],"In":[14],"this":[15],"paper,":[16],"we":[17,40,133],"attempt":[18],"exploit":[20],"the":[21,32,53,67,71,88,95,98],"knowledge":[22,44,51,85],"encoded":[23],"in":[24,36,80,103],"a":[25,42,57,77,81,116],"previously":[26],"trained":[27,54],"classification":[28],"model":[29,55,61,69,99],"handle":[31],"catas-trophic":[33],"forgetting":[34],"problem":[35],"continual":[37],"learning.":[38,105],"Specifically,":[39],"introduce":[41],"so-called":[43],"delegator,":[45],"which":[46],"capable":[48],"transferring":[50],"randomly":[58],"re-initialized":[59],"new":[60],"by":[62,87,115],"generating":[63],"informative":[64],"samples.":[65],"Given":[66],"previous":[68],"only,":[70],"delegator":[72,89],"effectively":[74],"learned":[75],"using":[76],"self-distillation":[78],"mechanism":[79],"data-free":[82],"manner.":[83],"The":[84],"extracted":[86],"then":[91],"utilized":[92],"maintain":[94],"performance":[96,136],"on":[100,119],"tasks":[102],"This":[106],"simple":[107],"framework":[110],"surpasses":[111],"existing":[112],"exemplar-free":[113],"methods":[114,140],"large":[117],"margin":[118],"four":[120],"widely":[121],"used":[122],"class":[123],"benchmarks,":[125],"namely":[126],"CIFAR-100,":[127],"ImageNet-Subset,":[128],"Caltech-101":[129],"and":[130],"Flowers-102.":[131],"Notably,":[132],"achieve":[134],"comparable":[135],"some":[138],"exemplar-based":[139],"without":[141],"accessing":[142],"any":[143],"exemplars.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}