{"id":"https://openalex.org/W3097010081","doi":"https://doi.org/10.1145/3421766.3421825","title":"AU Data Augmentation Method Based on Generative Adversarial Networks","display_name":"AU Data Augmentation Method Based on Generative Adversarial Networks","publication_year":2020,"publication_date":"2020-10-15","ids":{"openalex":"https://openalex.org/W3097010081","doi":"https://doi.org/10.1145/3421766.3421825","mag":"3097010081"},"language":"en","primary_location":{"id":"doi:10.1145/3421766.3421825","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3421766.3421825","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","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/A5053452031","display_name":"Qingdan Huang","orcid":"https://orcid.org/0009-0000-6791-3857"},"institutions":[{"id":"https://openalex.org/I4210090512","display_name":"Guangzhou Experimental Station","ror":"https://ror.org/00f2c2516","country_code":"CN","type":"facility","lineage":["https://openalex.org/I107851509","https://openalex.org/I4210090512","https://openalex.org/I4210127390","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingdan Huang","raw_affiliation_strings":["Electrical Power Test &amp; Research Institute of Guangzhou, Power Supply Bureau, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Power Test &amp; Research Institute of Guangzhou, Power Supply Bureau, Guangzhou, China","institution_ids":["https://openalex.org/I4210090512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079804758","display_name":"Liqiang Pei","orcid":null},"institutions":[{"id":"https://openalex.org/I4210090512","display_name":"Guangzhou Experimental Station","ror":"https://ror.org/00f2c2516","country_code":"CN","type":"facility","lineage":["https://openalex.org/I107851509","https://openalex.org/I4210090512","https://openalex.org/I4210127390","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liqiang Pei","raw_affiliation_strings":["Electrical Power Test &amp; Research Institute of Guangzhou, Power Supply Bureau, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Power Test &amp; Research Institute of Guangzhou, Power Supply Bureau, Guangzhou, China","institution_ids":["https://openalex.org/I4210090512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101767858","display_name":"Yong Wang","orcid":"https://orcid.org/0000-0002-6458-7256"},"institutions":[{"id":"https://openalex.org/I4210090512","display_name":"Guangzhou Experimental Station","ror":"https://ror.org/00f2c2516","country_code":"CN","type":"facility","lineage":["https://openalex.org/I107851509","https://openalex.org/I4210090512","https://openalex.org/I4210127390","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Wang","raw_affiliation_strings":["Electrical Power Test &amp; Research Institute of Guangzhou, Power Supply Bureau, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Power Test &amp; Research Institute of Guangzhou, Power Supply Bureau, Guangzhou, China","institution_ids":["https://openalex.org/I4210090512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014205100","display_name":"Lian Zeng","orcid":"https://orcid.org/0000-0003-0157-7111"},"institutions":[{"id":"https://openalex.org/I4210090512","display_name":"Guangzhou Experimental Station","ror":"https://ror.org/00f2c2516","country_code":"CN","type":"facility","lineage":["https://openalex.org/I107851509","https://openalex.org/I4210090512","https://openalex.org/I4210127390","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lian Zeng","raw_affiliation_strings":["Electrical Power Test &amp; Research Institute of Guangzhou, Power Supply Bureau, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Power Test &amp; Research Institute of Guangzhou, Power Supply Bureau, Guangzhou, China","institution_ids":["https://openalex.org/I4210090512"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210090512"],"apc_list":null,"apc_paid":null,"fwci":0.3577,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.66827702,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"243","last_page":"246"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9919000267982483,"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/T10057","display_name":"Face and Expression Recognition","score":0.9883999824523926,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6957809329032898},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6621989607810974},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6247484087944031},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.6079295873641968},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.511242151260376},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5068510174751282},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4941754639148712},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47370287775993347},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4679606854915619},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.4623485803604126},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.4580821096897125},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4518224895000458},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44212931394577026},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4191136360168457},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3881579339504242},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.38021740317344666},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23998087644577026},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1628182828426361},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11545705795288086}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6957809329032898},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6621989607810974},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6247484087944031},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6079295873641968},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.511242151260376},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5068510174751282},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4941754639148712},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47370287775993347},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4679606854915619},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.4623485803604126},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.4580821096897125},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4518224895000458},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44212931394577026},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4191136360168457},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3881579339504242},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.38021740317344666},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23998087644577026},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1628182828426361},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11545705795288086},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3421766.3421825","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3421766.3421825","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4300000071525574,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2052677124","https://openalex.org/W2096741982","https://openalex.org/W2139916508"],"related_works":["https://openalex.org/W2912987408","https://openalex.org/W4296978181","https://openalex.org/W2937381246","https://openalex.org/W4281672036","https://openalex.org/W4313444753","https://openalex.org/W4230582276","https://openalex.org/W2911822711","https://openalex.org/W4309969736","https://openalex.org/W2997921738","https://openalex.org/W2770818364"],"abstract_inverted_index":{"In":[0],"the":[1,13,20,28,43,49,70,78,81,83,93,98,106,147,156,161],"facial":[2],"action":[3,162],"unit":[4],"(Facial":[5],"Action":[6],"Unit,":[7],"AU)":[8],"recognition":[9,30],"process,":[10],"due":[11],"to":[12,75,92,96,145],"low":[14],"occurrence":[15],"probability":[16],"of":[17,80,130,150,160],"some":[18],"AUs,":[19],"sample":[21,157],"imbalance":[22],"is":[23,36],"serious,":[24],"which":[25],"severely":[26],"limits":[27],"model":[29,47,68,94,107,137],"performance.":[31],"Generative":[32],"adversarial":[33,86,143],"network":[34,87,144],"GAN":[35,67,110],"an":[37],"unsupervised":[38,50],"learning":[39,51],"method.":[40],"Compared":[41],"with":[42],"autoencoder":[44],"and":[45,61,72,102,126,154],"autoregressive":[46],"in":[48,105,115],"method,":[52],"its":[53],"advantages":[54],"are":[55],"sufficient":[56],"data":[57],"fitting,":[58],"higher":[59],"efficiency":[60],"better":[62],"generated":[63,99],"samples.":[64],"The":[65],"original":[66],"uses":[69],"minimum":[71],"maximum":[73],"(minmax)":[74],"continuously":[76],"optimize":[77],"training":[79,108],"model;":[82],"conditional":[84,90,141],"generation":[85,142],"CGAN":[88],"adds":[89],"constraints":[91],"input":[95],"make":[97],"results":[100],"controllable":[101],"prevent":[103],"collapse":[104],"process.":[109],"has":[111],"been":[112],"widely":[113],"used":[114],"research":[116],"fields":[117],"such":[118],"as":[119],"image":[120],"processing,":[121],"natural":[122],"language":[123],"processing":[124],"NLP,":[125],"real-time":[127],"color":[128],"correction":[129],"underwater":[131],"images.":[132],"This":[133],"paper":[134],"designs":[135],"a":[136,140,151],"based":[138],"on":[139],"supplement":[146],"minority":[148],"samples":[149],"specific":[152],"AU":[153],"improve":[155],"distribution":[158],"space":[159],"unit.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
