{"id":"https://openalex.org/W2963993763","doi":"https://doi.org/10.1109/cvpr.2018.00291","title":"CondenseNet: An Efficient DenseNet Using Learned Group Convolutions","display_name":"CondenseNet: An Efficient DenseNet Using Learned Group Convolutions","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2963993763","doi":"https://doi.org/10.1109/cvpr.2018.00291","mag":"2963993763"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2018.00291","doi":"https://doi.org/10.1109/cvpr.2018.00291","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2018.00291","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1711.09224","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013240918","display_name":"Gao Ming Huang","orcid":"https://orcid.org/0000-0002-7251-0988","observed_orcids":["https://orcid.org/0000-0002-7251-0988","https://orcid.org/0000-0002-0003-0184"]},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gao Huang","raw_affiliation_strings":["Cornell University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shichen Liu","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":"Shichen Liu","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":"https://openalex.org/A5109065097","display_name":"Laurens J. P. van der Maaten","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Laurens van der Maaten","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5006380227","display_name":"Kilian Q. Weinberger","orcid":"https://orcid.org/0009-0008-9313-7239","observed_orcids":["https://orcid.org/0009-0008-9313-7239"]},"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":"Kilian Q. Weinberger","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":35.0854,"has_fulltext":true,"cited_by_count":945,"citation_normalized_percentile":{"value":0.99708953,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2752","last_page":"2761"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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":0.9994999766349792,"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":9.999999747378752e-05,"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":0.0,"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/dense-connections","display_name":"dense connections","score":0.7519999742507935},{"id":"https://openalex.org/keywords/compact-convolutional-neural-networks","display_name":"compact convolutional neural networks","score":0.7329999804496765},{"id":"https://openalex.org/keywords/mobile-devices","display_name":"mobile devices","score":0.6269999742507935},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.6620000004768372},{"id":"https://openalex.org/keywords/feature-reuse","display_name":"feature reuse","score":0.9440000057220459},{"id":"https://openalex.org/keywords/network-architecture","display_name":"network architecture","score":0.5849999785423279}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8403072357177734},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.837593138217926},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6846885085105896},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6712729930877686},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6308812499046326},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.5862697958946228},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.43629318475723267},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.43537747859954834},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41542309522628784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39982056617736816},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3728073239326477},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35646963119506836},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3355281352996826},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3260144591331482},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2525321841239929},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2018.00291","doi":"https://doi.org/10.1109/cvpr.2018.00291","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2018.00291","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1711.09224","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1711.09224","pdf_url":"https://arxiv.org/pdf/1711.09224","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.1711.09224","doi":"https://doi.org/10.48550/arxiv.1711.09224","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1711.09224","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":"pmh:oai:arXiv.org:1711.09224","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1711.09224","pdf_url":"https://arxiv.org/pdf/1711.09224","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"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G2945879066","display_name":"RI: AF: Small: Collaborative Research: Differentially Private Learning: From Theory to Applications","funder_award_id":"1618134","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3131646775","display_name":"CAREER: New Directions for Metric Learning","funder_award_id":"1550179","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4504108201","display_name":null,"funder_award_id":"N00014-17-1","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G6132627802","display_name":"III: Small: Collaborative Research: Towards Interpretable Machine Learning","funder_award_id":"1525919","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6874815845","display_name":"TRIPODS: Data Science for Improved Decision-Making: Learning in the Context of Uncertainty, Causality, Privacy, and Network Structures","funder_award_id":"1740822","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G709468757","display_name":null,"funder_award_id":"N00014-17-1-2175","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G8284465405","display_name":"S&AS: INT: Inference, Reasoning, and Learning for Robust Autonomous Driving","funder_award_id":"1724282","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G8929337198","display_name":null,"funder_award_id":"N00014-17","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306078","display_name":"U.S. Department of Defense","ror":"https://ror.org/0447fe631"},{"id":"https://openalex.org/F4320306137","display_name":"Bill and Melinda Gates Foundation","ror":"https://ror.org/0456r8d26"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2963993763.pdf","grobid_xml":"https://content.openalex.org/works/W2963993763.grobid-xml"},"referenced_works_count":62,"referenced_works":["https://openalex.org/W1026270304","https://openalex.org/W1665214252","https://openalex.org/W1677182931","https://openalex.org/W1690739335","https://openalex.org/W1799366690","https://openalex.org/W1821462560","https://openalex.org/W1836465849","https://openalex.org/W1861492603","https://openalex.org/W2095705004","https://openalex.org/W2097117768","https://openalex.org/W2108598243","https://openalex.org/W2114766824","https://openalex.org/W2117539524","https://openalex.org/W2119144962","https://openalex.org/W2123045220","https://openalex.org/W2138019504","https://openalex.org/W2156150815","https://openalex.org/W2163605009","https://openalex.org/W2168894214","https://openalex.org/W2172166488","https://openalex.org/W2194775991","https://openalex.org/W2267635276","https://openalex.org/W2294370754","https://openalex.org/W2300242332","https://openalex.org/W2302255633","https://openalex.org/W2325237720","https://openalex.org/W2331143823","https://openalex.org/W2408279554","https://openalex.org/W2515385951","https://openalex.org/W2518108298","https://openalex.org/W2531409750","https://openalex.org/W2549139847","https://openalex.org/W2554931888","https://openalex.org/W2562731582","https://openalex.org/W2592790041","https://openalex.org/W2612445135","https://openalex.org/W2612983688","https://openalex.org/W2619096655","https://openalex.org/W2736372003","https://openalex.org/W2736941579","https://openalex.org/W2767421475","https://openalex.org/W2778955544","https://openalex.org/W2949117887","https://openalex.org/W2949650786","https://openalex.org/W2950179405","https://openalex.org/W2951134251","https://openalex.org/W2951583185","https://openalex.org/W2952922798","https://openalex.org/W2962851801","https://openalex.org/W2963125010","https://openalex.org/W2963363373","https://openalex.org/W2963446712","https://openalex.org/W2963674932","https://openalex.org/W2963911037","https://openalex.org/W2963975324","https://openalex.org/W2964137095","https://openalex.org/W2964299589","https://openalex.org/W3118608800","https://openalex.org/W4394670654","https://openalex.org/W6638444622","https://openalex.org/W6737496325","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W3072011699","https://openalex.org/W2963125010","https://openalex.org/W3096268690","https://openalex.org/W3190908553","https://openalex.org/W2964310721","https://openalex.org/W2807047086","https://openalex.org/W2964349401","https://openalex.org/W3174687992","https://openalex.org/W3002100544","https://openalex.org/W2996296973"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2,94],"are":[3,12,86],"increasingly":[4],"used":[5],"on":[6],"mobile":[7],"devices,":[8],"where":[9],"computational":[10],"resources":[11],"limited.":[13],"In":[14],"this":[15,58],"paper":[16],"we":[17],"develop":[18],"CondenseNet,":[19],"a":[20,32],"novel":[21,33],"network":[22],"architecture":[23],"with":[24,31],"unprecedented":[25],"efficiency.":[26],"It":[27],"combines":[28],"dense":[29,40],"connectivity":[30,41],"module":[34],"called":[35],"learned":[36,49],"group":[37,50,73],"convolution.":[38],"The":[39],"facilitates":[42],"feature":[43,59],"re-use":[44,60],"in":[45,79],"the":[46],"network,":[47],"whereas":[48],"convolutions":[51],"remove":[52],"connections":[53],"between":[54],"layers":[55],"for":[56,76],"which":[57],"is":[61],"superfluous.":[62],"At":[63],"test":[64],"time,":[65],"our":[66],"model":[67],"can":[68],"be":[69],"implemented":[70],"using":[71],"standard":[72],"convolutions,":[74],"allowing":[75],"efficient":[77,89],"computation":[78],"practice.":[80],"Our":[81],"experiments":[82],"show":[83],"that":[84],"CondenseNets":[85],"far":[87],"more":[88],"than":[90],"state-of-the-art":[91],"compact":[92],"convolutional":[93],"such":[95],"as":[96],"ShuffleNets.":[97]},"counts_by_year":[{"year":2026,"cited_by_count":35},{"year":2025,"cited_by_count":84},{"year":2024,"cited_by_count":95},{"year":2023,"cited_by_count":116},{"year":2022,"cited_by_count":137},{"year":2021,"cited_by_count":164},{"year":2020,"cited_by_count":157},{"year":2019,"cited_by_count":125},{"year":2018,"cited_by_count":32}],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2019-07-30T00:00:00"}
