{"id":"https://openalex.org/W4385482786","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191770","title":"RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network","display_name":"RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385482786","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191770"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10191770","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10191770","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2310.01157","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102912726","display_name":"Haozhe Sun","orcid":"https://orcid.org/0009-0000-7202-2223"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Haozhe Sun","raw_affiliation_strings":["LISN/CNRS/INRIA, Université Paris-Saclay,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LISN/CNRS/INRIA, Université Paris-Saclay,France","institution_ids":["https://openalex.org/I1326498283","https://openalex.org/I1294671590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006360335","display_name":"Isabelle Guyon","orcid":"https://orcid.org/0000-0002-9266-1783"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]},{"id":"https://openalex.org/I157066012","display_name":"Gleason (United States)","ror":"https://ror.org/03bw04561","country_code":"US","type":"company","lineage":["https://openalex.org/I157066012"]}],"countries":["FR","US"],"is_corresponding":false,"raw_author_name":"Isabelle Guyon","raw_affiliation_strings":["LISN/CNRS/INRIA, Université Paris-Saclay,France","Chalearn (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LISN/CNRS/INRIA, Université Paris-Saclay,France","institution_ids":["https://openalex.org/I1326498283","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"Chalearn (United States)","institution_ids":["https://openalex.org/I157066012"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032411139","display_name":"Felix Mohr","orcid":"https://orcid.org/0000-0002-9293-2424"},"institutions":[{"id":"https://openalex.org/I157650460","display_name":"Universidad de La Sabana","ror":"https://ror.org/02sqgkj21","country_code":"CO","type":"education","lineage":["https://openalex.org/I157650460"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Felix Mohr","raw_affiliation_strings":["Universidad de La Sabana,Colombia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad de La Sabana,Colombia","institution_ids":["https://openalex.org/I157650460"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107633038","display_name":"Hedi Tabia","orcid":"https://orcid.org/0000-0002-1827-7150"},"institutions":[{"id":"https://openalex.org/I4210107733","display_name":"Informatique, BioInformatique, Syst\u00e8mes Complexes","ror":"https://ror.org/01aqxgr98","country_code":"FR","type":"facility","lineage":["https://openalex.org/I277688954","https://openalex.org/I277688954","https://openalex.org/I4210107733","https://openalex.org/I88467170"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Hedi Tabia","raw_affiliation_strings":["IBISC, Univ. Evry, Université Paris-Saclay,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBISC, Univ. Evry, Université Paris-Saclay,France","institution_ids":["https://openalex.org/I4210107733"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5586,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.67603872,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9986000061035156,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9940000176429749,"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/backbone-network","display_name":"Backbone network","score":0.8187967538833618},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7994649410247803},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.7124475240707397},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6803591251373291},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.6797320246696472},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6697629690170288},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5630260705947876},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5213947892189026},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45008012652397156},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4399165213108063},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.42180532217025757},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34757083654403687},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.33779165148735046},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.08923530578613281},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08368399739265442}],"concepts":[{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.8187967538833618},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7994649410247803},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.7124475240707397},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6803591251373291},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.6797320246696472},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6697629690170288},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5630260705947876},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5213947892189026},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45008012652397156},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4399165213108063},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.42180532217025757},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34757083654403687},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.33779165148735046},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.08923530578613281},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08368399739265442},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C548081761","wikidata":"https://www.wikidata.org/wiki/Q180388","display_name":"Waste management","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10191770","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10191770","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2310.01157","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.01157","pdf_url":"https://arxiv.org/pdf/2310.01157","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"pmh:oai:HAL:hal-04224281v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04224281","pdf_url":"https://hal.science/hal-04224281/document","source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Joint Conference on Neural Networks (IJCNN 2023), Jun 2023, Gold Coast, Australia. pp.1-9, ⟨10.1109/IJCNN54540.2023.10191770⟩","raw_type":"Conference papers"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2310.01157","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.01157","pdf_url":"https://arxiv.org/pdf/2310.01157","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1045782842","display_name":null,"funder_award_id":"ANR-19-CHIA-0022","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G8318455765","display_name":null,"funder_award_id":"ANR-19","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"}],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"},{"id":"https://openalex.org/F4320338463","display_name":"CHIST-ERA","ror":"https://ror.org/00rbzpz17"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385482786.pdf"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W1530262073","https://openalex.org/W1936750108","https://openalex.org/W1982825626","https://openalex.org/W2050883661","https://openalex.org/W2100495367","https://openalex.org/W2117539524","https://openalex.org/W2140132917","https://openalex.org/W2163605009","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2480418144","https://openalex.org/W2570467259","https://openalex.org/W2593807705","https://openalex.org/W2607306668","https://openalex.org/W2741815235","https://openalex.org/W2788838181","https://openalex.org/W2951696358","https://openalex.org/W2962851801","https://openalex.org/W2962965870","https://openalex.org/W2963000224","https://openalex.org/W2963460174","https://openalex.org/W2964137095","https://openalex.org/W3160331490","https://openalex.org/W4212883601","https://openalex.org/W4221155125","https://openalex.org/W4252684946","https://openalex.org/W4287869615","https://openalex.org/W4288373825","https://openalex.org/W4295312788","https://openalex.org/W4308481882","https://openalex.org/W4377197552","https://openalex.org/W4385482786","https://openalex.org/W6631660994","https://openalex.org/W6677103964","https://openalex.org/W6684191040","https://openalex.org/W6713132643","https://openalex.org/W6725543821","https://openalex.org/W6726275242","https://openalex.org/W6737496325","https://openalex.org/W6742075157","https://openalex.org/W6748173244","https://openalex.org/W6756344106","https://openalex.org/W6757174645","https://openalex.org/W6766978945","https://openalex.org/W6803533579","https://openalex.org/W6810127701","https://openalex.org/W6849849255"],"related_works":["https://openalex.org/W4315697128","https://openalex.org/W3102845713","https://openalex.org/W2971502891","https://openalex.org/W3205506801","https://openalex.org/W4388335561","https://openalex.org/W4280599700","https://openalex.org/W2970530566","https://openalex.org/W4288261899","https://openalex.org/W4307309205","https://openalex.org/W4389115113"],"abstract_inverted_index":{"It":[0],"has":[1],"become":[2],"mainstream":[3],"in":[4],"computer":[5],"vision":[6],"and":[7,39,54,68,77,107,134,163,208],"other":[8],"machine":[9,32],"learning":[10,31],"domains":[11,187],"to":[12,50,95,126,165,171],"reuse":[13,96],"backbone":[14,59,79,99,115,200],"networks":[15],"pretrained":[16],"on":[17,45,117,176],"large":[18,67,113,178],"datasets":[19,184],"as":[20],"preprocessors.":[21],"Typically,":[22],"the":[23,35,101,148,158,193],"last":[24],"layer":[25],"is":[26,61,65],"replaced":[27],"by":[28,136],"a":[29,46,56,75,87,97,105,112,177,204],"shallow":[30],"of":[33,103,132,161,169,180,198],"sorts;":[34],"newly-added":[36],"classification":[37,183],"head":[38],"(optionally)":[40],"deeper":[41],"layers":[42],"are":[43],"fine-tuned":[44],"new":[47],"task.":[48],"Due":[49],"its":[51,130],"strong":[52],"performance":[53,82,143,194],"simplicity,":[55],"common":[57],"pre-trained":[58,98,116],"network":[60],"ResNet152.":[62],"However,":[63],"ResNet152":[64,114],"relatively":[66],"induces":[69],"inference":[70,210],"latency.":[71],"In":[72],"many":[73],"cases,":[74],"compact":[76],"efficient":[78],"with":[80,100],"similar":[81],"would":[83],"be":[84],"preferable":[85],"over":[86],"larger,":[88],"slower":[89],"one.":[90],"This":[91],"paper":[92],"investigates":[93],"techniques":[94,191],"objective":[102],"creating":[104],"smaller":[106,205],"faster":[108,209],"model.":[109],"Starting":[110],"from":[111,123,185],"ImageNet,":[118],"we":[119,146],"first":[120],"reduce":[121],"it":[122],"51":[124],"blocks":[125,152],"5":[127],"blocks,":[128],"reducing":[129],"number":[131,160],"parameters":[133,162],"FLOPs":[135],"more":[137],"than":[138],"6":[139],"times,":[140],"without":[141],"significant":[142],"degradation.":[144],"Then,":[145],"split":[147],"model":[149,206],"after":[150],"3":[151],"into":[153],"several":[154],"branches,":[155],"while":[156,202],"preserving":[157],"same":[159],"FLOPs,":[164],"create":[166],"an":[167],"ensemble":[168],"sub-networks":[170],"improve":[172],"performance.":[173],"Our":[174],"experiments":[175],"benchmark":[179],"40":[181],"image":[182],"various":[186],"suggest":[188],"that":[189],"our":[190],"match":[192],"(if":[195],"not":[196],"better)":[197],"\u201cclassical":[199],"fine-tuning\u201d":[201],"achieving":[203],"size":[207],"speed.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-17T08:01:34.144755","created_date":"2025-10-10T00:00:00"}