{"id":"https://openalex.org/W2969508501","doi":"https://doi.org/10.1109/access.2019.2936591","title":"A Fourier Domain Training Framework for Convolutional Neural Networks Based on the Fourier Domain Pyramid Pooling Method and Fourier Domain Exponential Linear Unit","display_name":"A Fourier Domain Training Framework for Convolutional Neural Networks Based on the Fourier Domain Pyramid Pooling Method and Fourier Domain Exponential Linear Unit","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2969508501","doi":"https://doi.org/10.1109/access.2019.2936591","mag":"2969508501"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2936591","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2936591","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08808902.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08808902.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jinhua Lin","orcid":"https://orcid.org/0000-0001-8683-8960"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinhua Lin","raw_affiliation_strings":["Machinery and Electronics Engineering, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Machinery and Electronics Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017116858","display_name":"Lin Ma","orcid":"https://orcid.org/0000-0002-7331-6132"},"institutions":[{"id":"https://openalex.org/I4210127843","display_name":"First Automotive Works (China)","ror":"https://ror.org/0353t4m91","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210127843"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Ma","raw_affiliation_strings":["FAW Foundry Company Ltd., Changchun, China"],"affiliations":[{"raw_affiliation_string":"FAW Foundry Company Ltd., Changchun, China","institution_ids":["https://openalex.org/I4210127843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042385997","display_name":"Yu Yao","orcid":"https://orcid.org/0000-0002-2329-2355"},"institutions":[{"id":"https://openalex.org/I4385474403","display_name":"Changchun University of Technology","ror":"https://ror.org/052pakb34","country_code":null,"type":"education","lineage":["https://openalex.org/I4385474403"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Yao","raw_affiliation_strings":["School of Applied Technology, Changchun University of Technology, Changchun, China"],"affiliations":[{"raw_affiliation_string":"School of Applied Technology, Changchun University of Technology, Changchun, China","institution_ids":["https://openalex.org/I4385474403"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.3006,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.85731472,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"7","issue":null,"first_page":"116612","last_page":"116631"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9961000084877014,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9961000084877014,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9943000078201294,"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/T10320","display_name":"Neural Networks and Applications","score":0.9943000078201294,"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":0.7175131440162659},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.6517391800880432},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.6093048453330994},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5978422164916992},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4788774847984314},{"id":"https://openalex.org/keywords/discrete-fourier-transform","display_name":"Discrete Fourier transform (general)","score":0.47303837537765503},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4440954923629761},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.43904635310173035},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.423827588558197},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.2529280483722687},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22407913208007812},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2086097002029419},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"Short-time Fourier transform","score":0.15167036652565002}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7175131440162659},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.6517391800880432},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.6093048453330994},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5978422164916992},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4788774847984314},{"id":"https://openalex.org/C57733114","wikidata":"https://www.wikidata.org/wiki/Q1006032","display_name":"Discrete Fourier transform (general)","level":5,"score":0.47303837537765503},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4440954923629761},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.43904635310173035},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.423827588558197},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.2529280483722687},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22407913208007812},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2086097002029419},{"id":"https://openalex.org/C166386157","wikidata":"https://www.wikidata.org/wiki/Q1477735","display_name":"Short-time Fourier transform","level":4,"score":0.15167036652565002},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2936591","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2936591","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08808902.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6b317966afc34d5e858f59893a1e1540","is_oa":true,"landing_page_url":"https://doaj.org/article/6b317966afc34d5e858f59893a1e1540","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 116612-116631 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2936591","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2936591","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08808902.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1231421488","display_name":null,"funder_award_id":"under","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2912022979","display_name":null,"funder_award_id":"51705032","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4558477445","display_name":null,"funder_award_id":"2018AA7031010B","funder_id":"https://openalex.org/F4320335773","funder_display_name":"National High-tech Research and Development Program"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335773","display_name":"National High-tech Research and Development Program","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2969508501.pdf","grobid_xml":"https://content.openalex.org/works/W2969508501.grobid-xml"},"referenced_works_count":58,"referenced_works":["https://openalex.org/W1005811612","https://openalex.org/W1665214252","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1789336918","https://openalex.org/W1839118408","https://openalex.org/W1849277567","https://openalex.org/W1921523184","https://openalex.org/W1979527452","https://openalex.org/W2016939324","https://openalex.org/W2035476608","https://openalex.org/W2061171222","https://openalex.org/W2069502471","https://openalex.org/W2102605133","https://openalex.org/W2123119012","https://openalex.org/W2142694332","https://openalex.org/W2155217764","https://openalex.org/W2163605009","https://openalex.org/W2163922914","https://openalex.org/W2172654076","https://openalex.org/W2179352600","https://openalex.org/W2194775991","https://openalex.org/W2559870345","https://openalex.org/W2560323025","https://openalex.org/W2560354825","https://openalex.org/W2565262489","https://openalex.org/W2604319603","https://openalex.org/W2626112510","https://openalex.org/W2727875856","https://openalex.org/W2742180631","https://openalex.org/W2751420734","https://openalex.org/W2758333670","https://openalex.org/W2762410434","https://openalex.org/W2783201053","https://openalex.org/W2793947836","https://openalex.org/W2795535209","https://openalex.org/W2809254203","https://openalex.org/W2884675507","https://openalex.org/W2951849131","https://openalex.org/W2962736495","https://openalex.org/W2962949934","https://openalex.org/W2963285578","https://openalex.org/W2963340555","https://openalex.org/W3098722327","https://openalex.org/W4248710273","https://openalex.org/W4294375521","https://openalex.org/W4302296459","https://openalex.org/W6637151318","https://openalex.org/W6637242042","https://openalex.org/W6638020065","https://openalex.org/W6638946568","https://openalex.org/W6639204139","https://openalex.org/W6640185926","https://openalex.org/W6640442106","https://openalex.org/W6682778277","https://openalex.org/W6684191040","https://openalex.org/W6685562342","https://openalex.org/W6687483927"],"related_works":["https://openalex.org/W4391621807","https://openalex.org/W4249847449","https://openalex.org/W44395729","https://openalex.org/W4321487865","https://openalex.org/W2765338038","https://openalex.org/W1496225612","https://openalex.org/W4313906399","https://openalex.org/W2516295475","https://openalex.org/W2766639751","https://openalex.org/W2390401148"],"abstract_inverted_index":{"convolutional":[0,53,86],"neural":[1,54,87],"networks":[2,55],"(CNNs)":[3],"in":[4,57,125],"the":[5,14,19,23,28,58,64,69,99,106,109,116,126,129,132,140,144,146,155,158,164,167,169,187,191,220,224,232,242,248,255,276,286,294],"frequency":[6,20,24],"domain":[7,25,39,82,95,101,148,251,258,281,296],"is":[8,31,98,108,151,228,261],"of":[9,27,157,190,205,241,290],"great":[10,237],"significance":[11],"for":[12,51,85,279],"extending":[13],"deep":[15,287,301],"learning":[16,288,302],"principle":[17],"to":[18,35,123,181,196,274,299],"domain.":[21,60],"However,":[22],"representation":[26,47,259,297],"convnet":[29],"architecture":[30],"highly":[32,262],"demanding":[33],"due":[34],"their":[36],"complicated":[37],"Fourier":[38,59,81,94,100,127,147,250,257,280,295],"training":[40,52,83,149,277],"features.":[41],"Therefore,":[42,266],"high":[43],"accuracy":[44,150,289],"and":[45,68,79,105,119,138,171,179,183,198,223,239,264,292],"unambiguous":[46],"strategies":[48],"are":[49,175],"needed":[50],"entirely":[56],"Being":[61],"founded":[62],"on":[63,207,231],"bin":[65],"decomposition":[66],"mechanism":[67],"non-saturated":[70],"activation":[71],"theory,":[72],"this":[73],"paper":[74],"proposes":[75],"an":[76,208],"accurate,":[77],"stable":[78],"efficient":[80,263],"framework":[84,90,192,253,273],"networks.":[88,303],"The":[89,113,216],"contains":[91],"two":[92],"important":[93],"representations:":[96],"one":[97],"exponential":[102],"linear":[103],"unit,":[104],"other":[107,300],"pyramid":[110],"pooling":[111],"layer.":[112],"former":[114],"alleviates":[115],"vanishing":[117],"phenomenon":[118],"makes":[120],"CNNs":[121,291],"easier":[122],"converge":[124],"domain;":[128],"latter":[130],"avoids":[131],"original":[133],"cropping":[134],"or":[135],"warping":[136],"steps":[137],"improves":[139],"classification":[141,173,221],"accuracy.":[142],"With":[143,163],"framework,":[145],"improved":[152],"without":[153],"sacrificing":[154],"throughput":[156],"graphic":[159],"processing":[160],"unit":[161],"(GPU).":[162],"Re-50":[165],"as":[166,270],"backbone,":[168],"top-1":[170],"top-5":[172],"errors":[174],"reduced":[176],"from":[177],"28.85":[178],"9.55":[180],"18.63":[182],"4.05,":[184],"respectively,":[185,200],"while":[186],"speedup":[188],"ratios":[189],"can":[193,284],"reach":[194],"up":[195],"4.9877":[197],"1.8997,":[199],"at":[201],"a":[202,271],"batch":[203],"size":[204],"128":[206],"NVIDIA":[209],"GEFORCE":[210],"RTX":[211],"2080":[212],"GPU":[213],"(8.92":[214],"TFLOPS).":[215],"average":[217],"difference":[218],"between":[219],"value":[222,227],"ground":[225],"truth":[226],"only":[229],"0.21":[230],"MetaGram-1":[233],"set,":[234],"which":[235,283],"indicates":[236],"goodness-of-fit":[238],"robustness":[240],"framework.":[243],"This":[244],"investigation":[245],"illustrates":[246],"that":[247],"proposed":[249],"CNN":[252],"using":[254],"sophisticated":[256],"strategy":[260,298],"accurate.":[265],"it":[267],"may":[268],"serve":[269],"baseline":[272],"establish":[275],"pipelines":[278],"CNNs,":[282],"improve":[285],"extend":[293]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":5}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}