{"id":"https://openalex.org/W4362684530","doi":"https://doi.org/10.2352/ei.2023.35.7.image-268","title":"Conditional synthetic food image generation","display_name":"Conditional synthetic food image generation","publication_year":2023,"publication_date":"2023-01-16","ids":{"openalex":"https://openalex.org/W4362684530","doi":"https://doi.org/10.2352/ei.2023.35.7.image-268"},"language":"en","primary_location":{"id":"doi:10.2352/ei.2023.35.7.image-268","is_oa":false,"landing_page_url":"https://doi.org/10.2352/ei.2023.35.7.image-268","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-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/A5112107954","display_name":"Wenjin Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wenjin Fu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083913830","display_name":"Yue Han","orcid":"https://orcid.org/0000-0003-4494-5455"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yue Han","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063620170","display_name":"Jiangpeng He","orcid":"https://orcid.org/0000-0002-8552-9880"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiangpeng He","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085323232","display_name":"Sriram Baireddy","orcid":"https://orcid.org/0000-0002-4386-0065"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sriram Baireddy","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101981312","display_name":"Mridul Gupta","orcid":"https://orcid.org/0009-0003-4343-4263"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mridul Gupta","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5001380619","display_name":"Fengqing Zhu","orcid":"https://orcid.org/0000-0002-3863-3220"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fengqing Zhu","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5112107954"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6174,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.68118612,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"35","issue":"7","first_page":"268","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.948199987411499,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.948199987411499,"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":0.7365171909332275},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6949014663696289},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.5142852663993835},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4911031723022461},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.4704793095588684},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.43929746747016907},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.41774600744247437},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.343992680311203},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.22592222690582275}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7365171909332275},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6949014663696289},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5142852663993835},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4911031723022461},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.4704793095588684},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.43929746747016907},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.41774600744247437},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.343992680311203},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.22592222690582275}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2352/ei.2023.35.7.image-268","is_oa":false,"landing_page_url":"https://doi.org/10.2352/ei.2023.35.7.image-268","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.5699999928474426,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W2922073769","https://openalex.org/W4378510483","https://openalex.org/W2490526372","https://openalex.org/W2989932438","https://openalex.org/W4387297750","https://openalex.org/W2186333919"],"abstract_inverted_index":{"Generative":[0],"Adversarial":[1],"Networks":[2],"(GAN)":[3],"have":[4],"been":[5],"widely":[6],"investigated":[7],"for":[8,39,59,76],"image":[9,31,41,78,130,152,199],"synthesis":[10],"based":[11],"on":[12,108,166],"their":[13],"powerful":[14],"representation":[15],"learning":[16],"ability.":[17],"In":[18],"this":[19],"work,":[20],"we":[21,63,81,99,136,184],"explore":[22,66],"the":[23,34,67,71,86,96,112,167,181,186,191,208],"StyleGAN":[24],"and":[25,50,56,69,94,123,150,170],"its":[26],"application":[27],"of":[28,37,73,126,174,189,193],"synthetic":[29,60,91,176,204],"food":[30,43,77,92,109,141,177,198],"generation.":[32,79],"Despite":[33],"impressive":[35],"performance":[36,72,106,192],"GAN":[38,74],"natural":[40],"generation,":[42],"images":[44,93,110,178],"suffer":[45],"from":[46,155],"high":[47],"intra-class":[48],"diversity":[49],"inter-class":[51,116],"similarity,":[52],"resulting":[53],"in":[54,207],"overfitting":[55],"visual":[57],"artifacts":[58],"images.":[61],"Therefore,":[62],"aim":[64],"to":[65,89,138,146,158],"capability":[68],"improve":[70],"methods":[75],"Specifically,":[80],"first":[82],"choose":[83],"StyleGAN3":[84],"as":[85,197],"baseline":[87],"method":[88,165],"generate":[90],"analyze":[95],"performance.":[97],"Then,":[98],"identify":[100],"two":[101],"issues":[102],"that":[103],"can":[104],"cause":[105],"degradation":[107],"during":[111,119,129],"training":[113,122,205],"phase:":[114],"(1)":[115],"feature":[117,148],"entanglement":[118,149],"multi-food":[120],"classes":[121],"(2)":[124],"loss":[125],"high-resolution":[127,156],"detail":[128],"downsampling.":[131],"To":[132],"address":[133],"both":[134],"issues,":[135],"propose":[137],"train":[139],"one":[140],"category":[142],"at":[143],"a":[144],"time":[145],"avoid":[147],"leverage":[151],"patches":[153],"cropped":[154],"datasets":[157],"retain":[159],"fine":[160],"details.":[161],"We":[162],"evaluate":[163],"our":[164],"Food-101":[168],"dataset":[169],"show":[171],"improved":[172],"quality":[173],"generated":[175],"compared":[179],"with":[180],"baseline.":[182],"Finally,":[183],"demonstrate":[185],"great":[187],"potential":[188],"improving":[190],"downstream":[194],"tasks,":[195],"such":[196],"classification":[200],"by":[201],"including":[202],"high-quality":[203],"samples":[206],"data":[209],"augmentation.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}