{"id":"https://openalex.org/W4361829703","doi":"https://doi.org/10.1109/ispacs57703.2022.10082842","title":"Connection Reduction of DenseNet for Image Recognition","display_name":"Connection Reduction of DenseNet for Image Recognition","publication_year":2022,"publication_date":"2022-11-22","ids":{"openalex":"https://openalex.org/W4361829703","doi":"https://doi.org/10.1109/ispacs57703.2022.10082842"},"language":"en","primary_location":{"id":"doi:10.1109/ispacs57703.2022.10082842","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ispacs57703.2022.10082842","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5032044053","display_name":"Rui\u2010Yang Ju","orcid":"https://orcid.org/0000-0003-2240-1377"},"institutions":[{"id":"https://openalex.org/I107470533","display_name":"Tamkang University","ror":"https://ror.org/04tft4718","country_code":"TW","type":"education","lineage":["https://openalex.org/I107470533"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Rui-Yang Ju","raw_affiliation_strings":["Tamkang University,Department of Electrical and Computer Engineering,New Taipei City,Taiwan,251301"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tamkang University,Department of Electrical and Computer Engineering,New Taipei City,Taiwan,251301","institution_ids":["https://openalex.org/I107470533"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025986552","display_name":"Jen\u2010Shiun Chiang","orcid":"https://orcid.org/0000-0001-7536-8967"},"institutions":[{"id":"https://openalex.org/I107470533","display_name":"Tamkang University","ror":"https://ror.org/04tft4718","country_code":"TW","type":"education","lineage":["https://openalex.org/I107470533"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jen-Shiun Chiang","raw_affiliation_strings":["Tamkang University,Department of Electrical and Computer Engineering,New Taipei City,Taiwan,251301"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tamkang University,Department of Electrical and Computer Engineering,New Taipei City,Taiwan,251301","institution_ids":["https://openalex.org/I107470533"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049306296","display_name":"Ray\u2010Bing Chen","orcid":"https://orcid.org/0000-0001-7226-509X"},"institutions":[{"id":"https://openalex.org/I107470533","display_name":"Tamkang University","ror":"https://ror.org/04tft4718","country_code":"TW","type":"education","lineage":["https://openalex.org/I107470533"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chih-Chia Chen","raw_affiliation_strings":["Tamkang University,Department of Electrical and Computer Engineering,New Taipei City,Taiwan,251301"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tamkang University,Department of Electrical and Computer Engineering,New Taipei City,Taiwan,251301","institution_ids":["https://openalex.org/I107470533"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001740781","display_name":"Yu-Shian Lin","orcid":"https://orcid.org/0000-0001-9825-9958"},"institutions":[{"id":"https://openalex.org/I107470533","display_name":"Tamkang University","ror":"https://ror.org/04tft4718","country_code":"TW","type":"education","lineage":["https://openalex.org/I107470533"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yu-Shian Lin","raw_affiliation_strings":["Tamkang University,Department of Electrical and Computer Engineering,New Taipei City,Taiwan,251301"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tamkang University,Department of Electrical and Computer Engineering,New Taipei City,Taiwan,251301","institution_ids":["https://openalex.org/I107470533"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I107470533"],"apc_list":null,"apc_paid":null,"fwci":0.1843,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.56599736,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9995999932289124,"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/T12676","display_name":"Machine Learning and ELM","score":0.9970999956130981,"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":0.7314504384994507},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6191816329956055},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6054402589797974},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5744143724441528},{"id":"https://openalex.org/keywords/connection","display_name":"Connection (principal bundle)","score":0.558914840221405},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.5521501302719116},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.510863721370697},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4988837242126465},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.47693556547164917},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4349517822265625},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3885079324245453},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37168657779693604},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3459686040878296},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.262428343296051},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11298039555549622},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.08360898494720459}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7314504384994507},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6191816329956055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6054402589797974},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5744143724441528},{"id":"https://openalex.org/C13355873","wikidata":"https://www.wikidata.org/wiki/Q2920850","display_name":"Connection (principal bundle)","level":2,"score":0.558914840221405},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.5521501302719116},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.510863721370697},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4988837242126465},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.47693556547164917},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4349517822265625},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3885079324245453},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37168657779693604},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3459686040878296},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.262428343296051},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11298039555549622},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.08360898494720459},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ispacs57703.2022.10082842","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ispacs57703.2022.10082842","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2335728318","https://openalex.org/W2766038477","https://openalex.org/W2796647784","https://openalex.org/W2796970379","https://openalex.org/W2910628332","https://openalex.org/W2963446712","https://openalex.org/W2987175876","https://openalex.org/W3113410735","https://openalex.org/W3118608800","https://openalex.org/W3175630421","https://openalex.org/W3179888767","https://openalex.org/W4214507171","https://openalex.org/W4221165620","https://openalex.org/W4285335674","https://openalex.org/W4312311293","https://openalex.org/W4312815172","https://openalex.org/W6745672167","https://openalex.org/W6749959846"],"related_works":["https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W2913146933","https://openalex.org/W2372385138","https://openalex.org/W4296359239","https://openalex.org/W2043093291","https://openalex.org/W2964954556","https://openalex.org/W3019910406"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"increase":[3],"depth":[4],"by":[5,99,139],"stacking":[6,23],"convolution":[7,24],"layers,":[8],"and":[9,33,72,95,117,128,148],"deeper":[10],"network":[11,30,40],"models":[12,48,150],"perform":[13,54],"better":[14],"in":[15,56,86,144],"image":[16,45,113],"recognition.":[17],"Empirical":[18],"research":[19],"shows":[20],"that":[21,120],"simply":[22],"layers":[25,85],"does":[26],"not":[27,64],"make":[28],"the":[29,44,96,100],"train":[31],"better,":[32],"skip":[34],"connection":[35],"(residual":[36],"learning)":[37],"can":[38],"improve":[39],"model":[41],"performance.":[42],"For":[43],"classification":[46,114],"tasks,":[47],"with":[49,141],"global":[50],"densely":[51,92],"connected":[52,93,98],"architectures":[53],"well":[55],"large":[57],"datasets":[58,68],"like":[59],"ImageNet,":[60],"but":[61],"they":[62],"are":[63,104,151],"suitable":[65],"for":[66],"small":[67],"such":[69],"as":[70],"CIFAR-10":[71,116],"SVHN.":[73],"Different":[74],"from":[75],"dense":[76],"connections,":[77],"we":[78],"propose":[79],"two":[80,101],"new":[81,102],"algorithms":[82,103],"to":[83],"connect":[84],"this":[87],"paper.":[88],"Baseline":[89],"is":[90],"a":[91,122],"network,":[94],"networks":[97],"named":[105],"ShortNet1and":[106],"ShortNet2,":[107],"respectively.":[108],"The":[109],"experimental":[110],"results":[111],"of":[112],"on":[115],"SVHN":[118],"show":[119],"ShortNet1has":[121],"5%":[123],"lower":[124],"test":[125,145],"error":[126],"rate":[127],"25%":[129],"faster":[130],"inference":[131,137],"time":[132,138],"than":[133],"Baseline.":[134],"ShortNet2speeds":[135],"up":[136],"40%":[140],"less":[142],"loss":[143],"accuracy.":[146],"Code":[147],"pretrained":[149],"available":[152],"at":[153],"https://github.com/RuiyangJu/Connection_Reduction/":[154]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}