{"id":"https://openalex.org/W4416250784","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227282","title":"Conventional Augmentation is More Effective than ImageGPT and GANs? A Comparison of Synthetic Data Evaluation Methods","display_name":"Conventional Augmentation is More Effective than ImageGPT and GANs? A Comparison of Synthetic Data Evaluation Methods","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250784","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227282"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5002496735","display_name":"Andrew Kennedy","orcid":null},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Andrew Kennedy","raw_affiliation_strings":["University of Exeter,Defence Data Research Centre,Exeter,United Kingdom"],"affiliations":[{"raw_affiliation_string":"University of Exeter,Defence Data Research Centre,Exeter,United Kingdom","institution_ids":["https://openalex.org/I23923803"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016900158","display_name":"Richard Everson","orcid":"https://orcid.org/0000-0002-3964-1150"},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Richard Everson","raw_affiliation_strings":["University of Exeter,Defence Data Research Centre,Exeter,United Kingdom"],"affiliations":[{"raw_affiliation_string":"University of Exeter,Defence Data Research Centre,Exeter,United Kingdom","institution_ids":["https://openalex.org/I23923803"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5002496735"],"corresponding_institution_ids":["https://openalex.org/I23923803"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37359623,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"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":0.4375,"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":0.4375,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.15369999408721924,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0778999999165535,"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/synthetic-data","display_name":"Synthetic data","score":0.7031999826431274},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6470999717712402},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4945000112056732},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4101000130176544},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.37299999594688416},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.36800000071525574},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3197000026702881},{"id":"https://openalex.org/keywords/experimental-data","display_name":"Experimental data","score":0.3089999854564667}],"concepts":[{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.7031999826431274},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6470999717712402},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6353999972343445},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5526000261306763},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4945000112056732},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43149998784065247},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37869998812675476},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.37299999594688416},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.36800000071525574},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2870999872684479},{"id":"https://openalex.org/C3018395757","wikidata":"https://www.wikidata.org/wiki/Q1379672","display_name":"Evaluation methods","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.27390000224113464},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":24,"referenced_works":["https://openalex.org/W2084404970","https://openalex.org/W2099471712","https://openalex.org/W2124299914","https://openalex.org/W2156163116","https://openalex.org/W2183341477","https://openalex.org/W2192203593","https://openalex.org/W2618530766","https://openalex.org/W2794022343","https://openalex.org/W2954996726","https://openalex.org/W2963185411","https://openalex.org/W3017919939","https://openalex.org/W3034664137","https://openalex.org/W3034733309","https://openalex.org/W3042183427","https://openalex.org/W3161307971","https://openalex.org/W3190683899","https://openalex.org/W4205206005","https://openalex.org/W4287883443","https://openalex.org/W4308733509","https://openalex.org/W4312694728","https://openalex.org/W4391782902","https://openalex.org/W4396898756","https://openalex.org/W4396918671","https://openalex.org/W4402727293"],"related_works":[],"abstract_inverted_index":{"Generative":[0],"synthetic":[1,27,105,129],"data":[2,13,28,39,62,106,148,184],"models":[3],"have":[4],"proven":[5],"effective":[6],"in":[7,156],"tasks":[8],"like":[9],"image":[10,48,150],"synthesis":[11],"and":[12,23,33,51,61,100,115,140,176],"augmentation.":[14],"This":[15],"study":[16],"aims":[17],"to":[18,80,103,135,143],"better":[19],"understand":[20],"the":[21,112,117,172,186],"effectiveness":[22],"performance":[24],"of":[25,38,58,73,93,128,147,174,182],"generative":[26],"generators":[29],"(SDGs),":[30],"specifically":[31],"ImageGPT":[32,113],"GANs,":[34],"against":[35],"conventional":[36,153],"methods":[37],"augmentation":[40,151,175],"for":[41,185],"downstream":[42,47,74,160,187],"supervised":[43],"learning.":[44],"We":[45,95],"evaluate":[46],"classifier":[49,75,161],"accuracy":[50,110,124],"Fr\u00e9chet":[52],"Inception":[53],"Distance":[54],"(FID)":[55],"as":[56],"measures":[57],"task":[59],"efficacy":[60],"fidelity.":[63],"While":[64,131],"FID":[65],"was":[66,69],"useful,":[67],"it":[68],"not":[70],"directly":[71],"predictive":[72],"performance,":[76],"with":[77,125,152],"its":[78],"relationship":[79],"classification":[81,109],"outcomes":[82],"varying":[83],"across":[84],"different":[85,91],"SDGs,":[86],"since":[87],"each":[88],"generator":[89],"introduced":[90],"types":[92],"dissimilarity.":[94],"found":[96],"that":[97],"conventional,":[98],"geometric":[99],"colour-space":[101],"transformations":[102,154,166],"generate":[104],"achieved":[107],"greater":[108,169],"than":[111],"model":[114,120],"only":[116],"best":[118],"GAN":[119],"could":[121],"match":[122],"this":[123],"moderate":[126],"amounts":[127],"data.":[130],"GANs":[132],"were":[133],"shown":[134],"be":[136],"a":[137,144,179],"useful":[138,183],"method,":[139],"are":[141],"applicable":[142],"wide":[145],"variety":[146],"types,":[149],"resulted":[155],"comparable":[157],"or":[158],"higher":[159],"accuracy.":[162],"In":[163],"addition,":[164],"these":[165],"also":[167],"provide":[168],"control":[170],"over":[171],"extent":[173],"can":[177],"produce":[178],"larger":[180],"amount":[181],"task.":[188]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}