{"id":"https://openalex.org/W3208750921","doi":"https://doi.org/10.1145/3459637.3482337","title":"Efficient Multi-Scale Feature Generation Adaptive Network","display_name":"Efficient Multi-Scale Feature Generation Adaptive Network","publication_year":2021,"publication_date":"2021-10-26","ids":{"openalex":"https://openalex.org/W3208750921","doi":"https://doi.org/10.1145/3459637.3482337","mag":"3208750921"},"language":"en","primary_location":{"id":"doi:10.1145/3459637.3482337","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482337","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"},"type":"article","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/A5054610619","display_name":"Gwanghan Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Gwanghan Lee","raw_affiliation_strings":["Sungkyunkwan University, Suwon, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University, Suwon, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063290249","display_name":"Minha Kim","orcid":"https://orcid.org/0000-0002-7178-0812"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Minha Kim","raw_affiliation_strings":["Sungkyunkwan University, Suwon, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University, Suwon, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063290249","display_name":"Minha Kim","orcid":"https://orcid.org/0000-0002-7178-0812"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Minha Kim","raw_affiliation_strings":["Sungkyunkwan University, Suwon, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University, Suwon, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033106393","display_name":"Simon S. Woo","orcid":"https://orcid.org/0000-0002-8983-1542"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Simon S. Woo","raw_affiliation_strings":["Sungkyunkwan University, Suwon, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University, Suwon, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5054610619"],"corresponding_institution_ids":["https://openalex.org/I848706"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13681373,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"883","last_page":"892"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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":0.9994000196456909,"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":0.9993000030517578,"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":0.8376002907752991},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.6589577794075012},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6146641969680786},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5985450148582458},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5548893213272095},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5279202461242676},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.522466778755188},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.49323147535324097},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41564127802848816},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38214099407196045},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38079944252967834},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.37351781129837036},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.11890074610710144}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8376002907752991},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6589577794075012},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6146641969680786},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5985450148582458},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5548893213272095},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5279202461242676},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.522466778755188},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.49323147535324097},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41564127802848816},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38214099407196045},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38079944252967834},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37351781129837036},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.11890074610710144},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3459637.3482337","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482337","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2183341477","https://openalex.org/W2193145675","https://openalex.org/W2194775991","https://openalex.org/W2267635276","https://openalex.org/W2382313035","https://openalex.org/W2463818697","https://openalex.org/W2752037867","https://openalex.org/W2884751099","https://openalex.org/W2946948417","https://openalex.org/W2962851801","https://openalex.org/W2962944050","https://openalex.org/W2963125010","https://openalex.org/W2963363373","https://openalex.org/W2963393494","https://openalex.org/W2963446712","https://openalex.org/W2963993763","https://openalex.org/W2964081807","https://openalex.org/W2964233199","https://openalex.org/W2981884310","https://openalex.org/W2987861506","https://openalex.org/W2996994879","https://openalex.org/W3035414587","https://openalex.org/W3035424951","https://openalex.org/W3035678286","https://openalex.org/W3106250896","https://openalex.org/W3118608800","https://openalex.org/W6603394118"],"related_works":["https://openalex.org/W1495042958","https://openalex.org/W2494338568","https://openalex.org/W2055243143","https://openalex.org/W3034421924","https://openalex.org/W2982536526","https://openalex.org/W4386858688","https://openalex.org/W4380302312","https://openalex.org/W3008689640","https://openalex.org/W4385338604","https://openalex.org/W3081626085"],"abstract_inverted_index":{"Recently,":[0],"an":[1,90],"early":[2,55,70,118],"exit":[3,119],"network,":[4],"which":[5,98],"dynamically":[6],"adjusts":[7],"the":[8,37,51,66,77,82,102,105,114,117,133,136,148,162,171],"model":[9,43,169],"complexity":[10],"during":[11],"inference":[12],"time,":[13],"has":[14],"achieved":[15],"remarkable":[16],"performance":[17,52,67,80,115],"and":[18,178],"neural":[19],"network":[20,79,142],"efficiency":[21],"to":[22,112,145],"be":[23],"used":[24],"for":[25,154],"various":[26],"applications.":[27],"So":[28],"far,":[29],"many":[30],"researchers":[31],"have":[32],"been":[33],"focusing":[34],"on":[35,76,175],"reducing":[36],"redundancy":[38,103],"of":[39,54,69,104,116,135,161],"input":[40],"sample":[41],"or":[42],"architecture.":[44],"However,":[45],"they":[46],"were":[47],"unsuccessful":[48],"at":[49,186],"resolving":[50],"drop":[53],"classifiers":[56,71],"that":[57,166],"make":[58],"predictions":[59],"with":[60],"insufficient":[61],"high-level":[62],"feature":[63,125,151],"information.":[64],"Consequently,":[65],"degradation":[68],"had":[72],"a":[73],"devastating":[74],"effect":[75],"entire":[78],"sharing":[81,130],"backbone.":[83],"Thus,":[84],"in":[85,132,158],"this":[86],"paper,":[87],"we":[88],"propose":[89],"Efficient":[91],"Multi-Scale":[92],"Feature":[93],"Generation":[94],"Adaptive":[95],"Network":[96],"(EMGNet),":[97],"not":[99],"only":[100],"reduced":[101],"architecture":[106],"but":[107],"also":[108],"generates":[109],"multi-scale":[110,124,150],"features":[111],"improve":[113],"network.":[120,163],"Our":[121],"approach":[122],"renders":[123],"generation":[126],"highly":[127],"efficient":[128],"through":[129],"weights":[131],"center":[134],"convolution":[137,156],"kernel.":[138],"Also,":[139],"our":[140,167],"gating":[141],"effectively":[143],"learns":[144],"automatically":[146],"determine":[147],"proper":[149],"ratio":[152],"required":[153],"each":[155],"layer":[157],"different":[159],"locations":[160],"We":[164],"demonstrate":[165],"proposed":[168],"outperforms":[170],"state-of-the-art":[172],"adaptive":[173],"networks":[174],"CIFAR10,":[176],"CIFAR100,":[177],"ImageNet":[179],"datasets.":[180],"The":[181],"implementation":[182],"code":[183],"is":[184],"available":[185],"https://github.com/lee-gwang/EMGNet":[187]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}