{"id":"https://openalex.org/W2981637358","doi":"https://doi.org/10.1109/cbmi.2019.8877387","title":"GANEx: A complete pipeline of training, inference and benchmarking GAN experiments","display_name":"GANEx: A complete pipeline of training, inference and benchmarking GAN experiments","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2981637358","doi":"https://doi.org/10.1109/cbmi.2019.8877387","mag":"2981637358"},"language":"en","primary_location":{"id":"doi:10.1109/cbmi.2019.8877387","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbmi.2019.8877387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Content-Based Multimedia Indexing (CBMI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/10642/8497","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034317141","display_name":"Vajira Thambawita","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153474","display_name":"Simula Metropolitan Center for Digital Engineering","ror":"https://ror.org/04xtarr15","country_code":"NO","type":"nonprofit","lineage":["https://openalex.org/I184531372","https://openalex.org/I2799829267","https://openalex.org/I4210153474"]}],"countries":["NO"],"is_corresponding":true,"raw_author_name":"Vajira Thambawita","raw_affiliation_strings":["SimulaMet, Norway"],"affiliations":[{"raw_affiliation_string":"SimulaMet, Norway","institution_ids":["https://openalex.org/I4210153474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001144517","display_name":"Hugo L. Hammer","orcid":"https://orcid.org/0000-0001-9429-7148"},"institutions":[{"id":"https://openalex.org/I184531372","display_name":"OsloMet \u2013 Oslo Metropolitan University","ror":"https://ror.org/04q12yn84","country_code":"NO","type":"education","lineage":["https://openalex.org/I184531372"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Hugo Lewi Hammer","raw_affiliation_strings":["Oslo Metropolitan University, Norway"],"affiliations":[{"raw_affiliation_string":"Oslo Metropolitan University, Norway","institution_ids":["https://openalex.org/I184531372"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102968267","display_name":"Michael A. Riegler","orcid":"https://orcid.org/0000-0002-3153-2064"},"institutions":[{"id":"https://openalex.org/I4210153474","display_name":"Simula Metropolitan Center for Digital Engineering","ror":"https://ror.org/04xtarr15","country_code":"NO","type":"nonprofit","lineage":["https://openalex.org/I184531372","https://openalex.org/I2799829267","https://openalex.org/I4210153474"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Michael Riegler","raw_affiliation_strings":["SimulaMet, Norway"],"affiliations":[{"raw_affiliation_string":"SimulaMet, Norway","institution_ids":["https://openalex.org/I4210153474"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088962741","display_name":"P\u00e5l Halvorsen","orcid":"https://orcid.org/0000-0003-2073-7029"},"institutions":[{"id":"https://openalex.org/I184531372","display_name":"OsloMet \u2013 Oslo Metropolitan University","ror":"https://ror.org/04q12yn84","country_code":"NO","type":"education","lineage":["https://openalex.org/I184531372"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Pal Halvorsen","raw_affiliation_strings":["Oslo Metropolitan University, Norway"],"affiliations":[{"raw_affiliation_string":"Oslo Metropolitan University, Norway","institution_ids":["https://openalex.org/I184531372"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5034317141"],"corresponding_institution_ids":["https://openalex.org/I4210153474"],"apc_list":null,"apc_paid":null,"fwci":0.3037,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.61156457,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":"abs 1411 1784","issue":null,"first_page":"1","last_page":"4"},"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":0.9993000030517578,"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":0.9993000030517578,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9968000054359436,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8508814573287964},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7699135541915894},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.7140056490898132},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6458156108856201},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5569097995758057},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5258629322052002},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5157511830329895},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5070321559906006},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.5052576661109924},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.49535810947418213},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.43057823181152344},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4036550223827362}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8508814573287964},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7699135541915894},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7140056490898132},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6458156108856201},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5569097995758057},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5258629322052002},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5157511830329895},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5070321559906006},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.5052576661109924},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.49535810947418213},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.43057823181152344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4036550223827362},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cbmi.2019.8877387","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbmi.2019.8877387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Content-Based Multimedia Indexing (CBMI)","raw_type":"proceedings-article"},{"id":"pmh:oai:oda.hioa.no:10642/8497","is_oa":true,"landing_page_url":"https://hdl.handle.net/10642/8497","pdf_url":null,"source":{"id":"https://openalex.org/S4306401716","display_name":"Duo Research Archive (University of Oslo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184942183","host_organization_name":"University of Oslo","host_organization_lineage":["https://openalex.org/I184942183"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"978-1-7281-4673-7","raw_type":"info:eu-repo/semantics/bookPart"}],"best_oa_location":{"id":"pmh:oai:oda.hioa.no:10642/8497","is_oa":true,"landing_page_url":"https://hdl.handle.net/10642/8497","pdf_url":null,"source":{"id":"https://openalex.org/S4306401716","display_name":"Duo Research Archive (University of Oslo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184942183","host_organization_name":"University of Oslo","host_organization_lineage":["https://openalex.org/I184942183"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"978-1-7281-4673-7","raw_type":"info:eu-repo/semantics/bookPart"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1570448133","https://openalex.org/W1834627138","https://openalex.org/W2099471712","https://openalex.org/W2112796928","https://openalex.org/W2125389028","https://openalex.org/W2181347294","https://openalex.org/W2577946330","https://openalex.org/W2606833507","https://openalex.org/W2617585083","https://openalex.org/W2888723145","https://openalex.org/W2899771611","https://openalex.org/W2911542279","https://openalex.org/W2953384591","https://openalex.org/W2962793481","https://openalex.org/W2963684088","https://openalex.org/W2963784072","https://openalex.org/W4320013936","https://openalex.org/W6678815747","https://openalex.org/W6685352114","https://openalex.org/W6685777803","https://openalex.org/W6713134421","https://openalex.org/W6720691552","https://openalex.org/W6732248266"],"related_works":["https://openalex.org/W2750279955","https://openalex.org/W4295101899","https://openalex.org/W2342475854","https://openalex.org/W2294923432","https://openalex.org/W2963644502","https://openalex.org/W2971572189","https://openalex.org/W2341138520","https://openalex.org/W2137657024","https://openalex.org/W197206975","https://openalex.org/W2208878950"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"(DL)":[2],"is":[3,59,94,153,231],"one":[4,93],"of":[5,12,36,115,128,142,150,183,198,249],"the":[6,10,40,54,126,132,148,163,180,247],"standard":[7],"methods":[8],"in":[9,64,97,162],"field":[11],"multimedia":[13],"research":[14],"to":[15,42,71,125,218,234,245],"perform":[16,80],"data":[17,51,85,103],"classification,":[18],"detection,":[19],"segmentation":[20,81],"and":[21,32,61,140,145,159,165,186,192,225,239],"generation.":[22],"Within":[23],"DL,":[24],"generative":[25,151,207],"adversarial":[26],"networks":[27],"(GANs)":[28],"represents":[29],"a":[30,113,173,199,214,235],"new":[31,50,174],"highly":[33,62,154],"popular":[34,60],"branch":[35],"methods.":[37],"GANs":[38,66,118,188],"have":[39],"capability":[41],"generate,":[43],"from":[44],"random":[45],"noise":[46],"or":[47,82],"conditional":[48],"input,":[49],"realizations":[52],"within":[53],"dataset":[55],"population.":[56],"While":[57],"generation":[58],"useful":[63,70],"itself,":[65],"can":[67,119,240],"also":[68],"be":[69,120,241],"improve":[72],"supervised":[73],"DL.":[74],"GAN-based":[75],"approaches":[76],"can,":[77],"for":[78,86,178],"example,":[79],"create":[83],"synthetic":[84],"training":[87,102],"other":[88],"DL":[89,237],"models.":[90],"The":[91,195,228],"latter":[92],"especially":[95],"interesting":[96],"domains":[98],"where":[99],"not":[100,232],"much":[101],"exists":[104],"such":[105,135],"as":[106,136],"medical":[107],"multimedia.":[108],"In":[109,168],"this":[110,169],"respect,":[111],"performing":[112],"series":[114],"experiments":[116],"involving":[117],"very":[121,209],"time":[122],"consuming":[123],"due":[124],"lack":[127],"tools":[129],"that":[130],"support":[131,219],"whole":[133,181],"pipeline":[134,182],"structured":[137,220],"training,":[138,184],"testing":[139],"tracking":[141],"different":[143],"architectures":[144],"configurations.":[146],"Moreover,":[147,211],"success":[149],"models":[152,208],"dependent":[155],"on":[156],"hyper-parameter":[157,223],"optimization":[158],"statistical":[160],"analysis":[161],"design":[164],"fine-tuning":[166],"stages.":[167],"paper,":[170],"we":[171],"present":[172],"tool":[175,196,230],"called":[176,202],"GANEx":[177,212],"making":[179],"inference":[185],"benchmarking":[187],"faster,":[189],"more":[190,193],"efficient":[191],"structured.":[194],"consists":[197],"special":[200],"library":[201],"FastGAN":[203],"which":[204],"allows":[205],"designing":[206],"fast.":[210],"has":[213],"graphical":[215],"user":[216],"interface":[217],"experimenting,":[221],"quick":[222],"configurations":[224],"output":[226],"analysis.":[227],"presented":[229],"limited":[233],"specific":[236],"framework":[238],"therefore":[242],"even":[243],"used":[244],"compare":[246],"performance":[248],"cross":[250],"frameworks.":[251]},"counts_by_year":[{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}