{"id":"https://openalex.org/W4401023950","doi":"https://doi.org/10.24963/ijcai.2024/514","title":"Revisiting Neural Networks for Continual Learning: An Architectural Perspective","display_name":"Revisiting Neural Networks for Continual Learning: An Architectural Perspective","publication_year":2024,"publication_date":"2024-07-26","ids":{"openalex":"https://openalex.org/W4401023950","doi":"https://doi.org/10.24963/ijcai.2024/514"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2024/514","doi":"https://doi.org/10.24963/ijcai.2024/514","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2024/514","pdf_url":"https://www.ijcai.org/proceedings/2024/0514.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.ijcai.org/proceedings/2024/0514.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Aojun Lu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aojun Lu","raw_affiliation_strings":["Sichuan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Tao Feng","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Feng","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hangjie Yuan","orcid":null,"observed_orcids":[]},"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":"Hangjie Yuan","raw_affiliation_strings":["Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032444986","display_name":"Xiaotian Song","orcid":"https://orcid.org/0009-0004-2924-3070","observed_orcids":["https://orcid.org/0009-0004-2924-3070"]},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaotian Song","raw_affiliation_strings":["Sichuan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yanan Sun","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanan Sun","raw_affiliation_strings":["Sichuan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University","institution_ids":["https://openalex.org/I24185976"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7323,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.86253053,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4651","last_page":"4659"},"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":0.9984999895095825,"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":0.9984999895095825,"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":0.0007999999797903001,"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/T10320","display_name":"Neural Networks and Applications","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/continual-learning","display_name":"continual learning","score":0.9990000128746033},{"id":"https://openalex.org/keywords/catastrophic-forgetting","display_name":"catastrophic forgetting","score":0.996999979019165},{"id":"https://openalex.org/keywords/neural-network-architecture","display_name":"neural network architecture","score":0.8090000152587891},{"id":"https://openalex.org/keywords/network-depth","display_name":"network depth","score":0.7680000066757202},{"id":"https://openalex.org/keywords/network-width","display_name":"network width","score":0.9929999709129333},{"id":"https://openalex.org/keywords/skip-connections","display_name":"skip connections","score":0.9760000109672546},{"id":"https://openalex.org/keywords/global-pooling","display_name":"global pooling","score":0.8949999809265137},{"id":"https://openalex.org/keywords/downsampling","display_name":"downsampling","score":0.8899999856948853},{"id":"https://openalex.org/keywords/task-incremental-learning","display_name":"task-incremental learning","score":0.7440000176429749},{"id":"https://openalex.org/keywords/class-incremental-learning","display_name":"class-incremental learning","score":0.906000018119812},{"id":"https://openalex.org/keywords/alexnet","display_name":"AlexNet","score":0.8119999766349792},{"id":"https://openalex.org/keywords/resnet","display_name":"ResNet","score":1.0}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6876502633094788},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.6678171753883362},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5293745994567871},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41345077753067017}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.24963/ijcai.2024/514","doi":"https://doi.org/10.24963/ijcai.2024/514","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2024/514","pdf_url":"https://www.ijcai.org/proceedings/2024/0514.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2404.14829","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2404.14829","pdf_url":"https://arxiv.org/pdf/2404.14829","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":"doi:10.48550/arxiv.2404.14829","doi":"https://doi.org/10.48550/arxiv.2404.14829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2404.14829","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:10.24963/ijcai.2024/514","doi":"https://doi.org/10.24963/ijcai.2024/514","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2024/514","pdf_url":"https://www.ijcai.org/proceedings/2024/0514.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G524008204","display_name":null,"funder_award_id":"62276175","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4401023950.pdf","grobid_xml":"https://content.openalex.org/works/W4401023950.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4416131215","https://openalex.org/W4226325574","https://openalex.org/W4214818106","https://openalex.org/W7168148530","https://openalex.org/W7125909335","https://openalex.org/W7217264886","https://openalex.org/W4386065654","https://openalex.org/W4407386455","https://openalex.org/W7171307946","https://openalex.org/W4404402399"],"abstract_inverted_index":{"Efforts":[0],"to":[1,22,38,43,54,132],"overcome":[2],"catastrophic":[3],"forgetting":[4],"have":[5],"primarily":[6],"centered":[7],"around":[8],"developing":[9],"more":[10,169],"effective":[11,129],"Continual":[12],"Learning":[13],"(CL)":[14],"methods.":[15],"In":[16,95],"contrast,":[17],"less":[18],"attention":[19],"was":[20],"devoted":[21],"analyzing":[23],"the":[24,60,73,84,176],"role":[25],"of":[26,62,161,173],"network":[27,31,48,63,74,85],"architecture":[28,49,70,179],"design":[29,50,71],"(e.g.,":[30],"depth,":[32,80],"width,":[33],"and":[34,51,53,79,81,93,123,150,167,183],"components)":[35],"in":[36,112,171,180],"contributing":[37],"CL.":[39,66,109],"This":[40,67],"paper":[41],"seeks":[42],"bridge":[44],"this":[45,138],"gap":[46],"between":[47],"CL,":[52],"present":[55],"a":[56,117,126,134],"holistic":[57],"study":[58],"on":[59,65],"impact":[61],"architectures":[64,155],"work":[68],"considers":[69],"at":[72,83,189],"scaling":[75],"level,":[76],"i.e.,":[77,87],"width":[78],"also":[82],"components,":[86],"skip":[88],"connections,":[89],"global":[90],"pooling":[91],"layers,":[92],"down-sampling.":[94],"both":[96],"cases,":[97],"we":[98,115],"first":[99],"derive":[100],"insights":[101],"through":[102],"systematically":[103],"exploring":[104],"how":[105],"architectural":[106],"designs":[107],"affect":[108],"Then,":[110],"grounded":[111],"these":[113],"insights,":[114],"craft":[116],"specialized":[118],"search":[119],"space":[120],"for":[121],"CL":[122,148,162,178],"further":[124],"propose":[125],"simple":[127],"yet":[128],"ArchCraft":[130],"method":[131,139],"steer":[133],"CL-friendly":[135],"architecture,":[136],"namely,":[137],"recrafts":[140],"AlexNet/ResNet":[141],"into":[142],"AlexAC/ResAC.":[143],"Experimental":[144],"validation":[145],"across":[146],"various":[147],"settings":[149],"scenarios":[151],"demonstrates":[152],"that":[153],"improved":[154],"are":[156],"parameter-efficient,":[157],"achieving":[158],"state-of-the-art":[159],"performance":[160],"while":[163],"being":[164],"86%,":[165],"61%,":[166],"97%":[168],"compact":[170],"terms":[172],"parameters":[174],"than":[175],"naive":[177],"Task":[181],"IL":[182],"Class":[184],"IL.":[185],"Code":[186],"is":[187],"available":[188],"https://github.com/byyx666/ArchCraft.":[190]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":4}],"updated_date":"2026-10-26T11:51:55.731654","created_date":"2024-07-27T00:00:00"}