{"id":"https://openalex.org/W2945472816","doi":"https://doi.org/10.1109/tpami.2019.2918284","title":"Convolutional Networks with Dense Connectivity","display_name":"Convolutional Networks with Dense Connectivity","publication_year":2019,"publication_date":"2019-05-23","ids":{"openalex":"https://openalex.org/W2945472816","doi":"https://doi.org/10.1109/tpami.2019.2918284","mag":"2945472816","pmid":"https://pubmed.ncbi.nlm.nih.gov/31135351"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2019.2918284","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2019.2918284","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5013240918","display_name":"Gao Huang","orcid":"https://orcid.org/0000-0002-7251-0988"},"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":"Gao Huang","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7251-0988","affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100452089","display_name":"Zhuang Liu","orcid":"https://orcid.org/0000-0001-6395-0212"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhuang Liu","raw_affiliation_strings":["UC Berkeley, Berkeley Artificial Intelligence Research, Berkeley, CA, USA"],"raw_orcid":"https://orcid.org/0000-0001-6395-0212","affiliations":[{"raw_affiliation_string":"UC Berkeley, Berkeley Artificial Intelligence Research, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065203625","display_name":"Geoff Pleiss","orcid":null},"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":"Geoff Pleiss","raw_affiliation_strings":["Department of Computer Science, Cornell University, Ithaca, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109065097","display_name":"Laurens van der Maaten","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Laurens van der Maaten","raw_affiliation_strings":["Facebook AI Research, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facebook AI Research, New York, NY, USA","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006380227","display_name":"Kilian Q. Weinberger","orcid":null},"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":"Kilian Q. Weinberger","raw_affiliation_strings":["Department of Computer Science, Cornell University, Ithaca, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":34.3093,"has_fulltext":false,"cited_by_count":585,"citation_normalized_percentile":{"value":0.99775163,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"44","issue":"12","first_page":"8704","last_page":"8716"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9990000128746033,"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/T12676","display_name":"Machine Learning and ELM","score":0.9990000128746033,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9969000220298767,"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/computer-science","display_name":"Computer science","score":0.7017825245857239},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.59424889087677},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5102980136871338},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35843902826309204}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7017825245857239},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.59424889087677},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5102980136871338},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35843902826309204}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2019.2918284","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2019.2918284","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:31135351","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31135351","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5}],"awards":[{"id":"https://openalex.org/G3663163280","display_name":null,"funder_award_id":"III-1526012","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"},{"id":"https://openalex.org/G5531073821","display_name":null,"funder_award_id":"IIS-1149882","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"},{"id":"https://openalex.org/G6023497758","display_name":null,"funder_award_id":"IIS-1724282","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"},{"id":"https://openalex.org/G6685178775","display_name":null,"funder_award_id":"III-1618134","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"},{"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"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306137","display_name":"Bill and Melinda Gates Foundation","ror":"https://ror.org/0456r8d26"},{"id":"https://openalex.org/F4320307999","display_name":"SAP North America","ror":"https://ror.org/01gbhv690"},{"id":"https://openalex.org/F4320335353","display_name":"National Science Foundation of Sri Lanka","ror":"https://ror.org/010xaa060"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":71,"referenced_works":["https://openalex.org/W104184427","https://openalex.org/W300523764","https://openalex.org/W1677182931","https://openalex.org/W1690739335","https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W1948751323","https://openalex.org/W2095705004","https://openalex.org/W2097117768","https://openalex.org/W2102013737","https://openalex.org/W2108598243","https://openalex.org/W2109779438","https://openalex.org/W2112796928","https://openalex.org/W2116456623","https://openalex.org/W2117539524","https://openalex.org/W2123045220","https://openalex.org/W2147800946","https://openalex.org/W2156387975","https://openalex.org/W2162741153","https://openalex.org/W2163605009","https://openalex.org/W2168894214","https://openalex.org/W2174554700","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2279098554","https://openalex.org/W2302255633","https://openalex.org/W2331143823","https://openalex.org/W2333796428","https://openalex.org/W2335728318","https://openalex.org/W2337199865","https://openalex.org/W2338908902","https://openalex.org/W2408279554","https://openalex.org/W2464772092","https://openalex.org/W2531409750","https://openalex.org/W2559597482","https://openalex.org/W2752782242","https://openalex.org/W2759532730","https://openalex.org/W2766038477","https://openalex.org/W2950621961","https://openalex.org/W2952229419","https://openalex.org/W2963993763","https://openalex.org/W2964137095","https://openalex.org/W3118608800","https://openalex.org/W6604254268","https://openalex.org/W6623329352","https://openalex.org/W6626481562","https://openalex.org/W6637551013","https://openalex.org/W6638444622","https://openalex.org/W6638667902","https://openalex.org/W6674330103","https://openalex.org/W6676553272","https://openalex.org/W6677681515","https://openalex.org/W6677995690","https://openalex.org/W6682889407","https://openalex.org/W6684191040","https://openalex.org/W6684665197","https://openalex.org/W6685251022","https://openalex.org/W6695314431","https://openalex.org/W6696761078","https://openalex.org/W6703049675","https://openalex.org/W6703116779","https://openalex.org/W6703652217","https://openalex.org/W6703857926","https://openalex.org/W6713132643","https://openalex.org/W6713596413","https://openalex.org/W6714181750","https://openalex.org/W6719523400","https://openalex.org/W6740164494","https://openalex.org/W6744843382","https://openalex.org/W6745672167","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Recent":[0],"work":[1],"has":[2,78],"shown":[3],"that":[4],"convolutional":[5,62],"networks":[6,63],"can":[7],"be":[8],"substantially":[9,123],"deeper,":[10],"more":[11],"accurate,":[12],"and":[13,28,41,73,97,122,143,160],"efficient":[14],"to":[15,25,31,52,162],"train":[16],"if":[17],"they":[18,114],"contain":[19],"shorter":[20],"connections":[21],"between":[22,70],"layers":[23,66,92],"close":[24,30],"the":[26,32,43,87,116,150],"input":[27],"those":[29],"output.":[33],"In":[34],"this":[35,39],"paper,":[36],"we":[37],"embrace":[38],"observation":[40],"introduce":[42],"Dense":[44],"Convolutional":[45],"Network":[46],"(DenseNet),":[47],"which":[48],"connects":[49],"each":[50,71,85],"layer":[51,55,72],"every":[53],"other":[54],"in":[56],"a":[57],"feed-forward":[58],"fashion.":[59],"Whereas":[60],"traditional":[61],"with":[64],"L":[65,68],"have":[67,110],"connections-one":[69],"its":[74,98],"subsequent":[75,107],"layer-our":[76],"network":[77],"[Formula:":[79],"see":[80],"text]":[81],"direct":[82],"connections.":[83],"For":[84],"layer,":[86],"feature-maps":[88,100],"of":[89,154],"all":[90,106],"preceding":[91],"are":[93,101],"used":[94,102],"as":[95,103],"inputs,":[96],"own":[99],"inputs":[104],"into":[105],"layers.":[108],"DenseNets":[109,145],"several":[111],"compelling":[112],"advantages:":[113],"alleviate":[115],"vanishing-gradient":[117],"problem,":[118],"encourage":[119],"feature":[120],"reuse":[121],"improve":[124],"parameter":[125],"efficiency.":[126],"We":[127],"evaluate":[128],"our":[129],"proposed":[130],"architecture":[131],"on":[132,152],"four":[133],"highly":[134],"competitive":[135],"object":[136],"recognition":[137],"benchmark":[138],"tasks":[139],"(CIFAR-10,":[140],"CIFAR-100,":[141],"SVHN,":[142],"ImageNet).":[144],"obtain":[146],"significant":[147],"improvements":[148],"over":[149],"state-of-the-art":[151],"most":[153],"them,":[155],"whilst":[156],"requiring":[157],"less":[158],"parameters":[159],"computation":[161],"achieve":[163],"high":[164],"performance.":[165]},"counts_by_year":[{"year":2026,"cited_by_count":40},{"year":2025,"cited_by_count":97},{"year":2024,"cited_by_count":126},{"year":2023,"cited_by_count":107},{"year":2022,"cited_by_count":71},{"year":2021,"cited_by_count":91},{"year":2020,"cited_by_count":53}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}