{"id":"https://openalex.org/W3200445570","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533969","title":"BTGAN: Training GAN with Balanced Triplet Loss and Two-Branch Architecture","display_name":"BTGAN: Training GAN with Balanced Triplet Loss and Two-Branch Architecture","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3200445570","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533969","mag":"3200445570"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5029249313","display_name":"Simin Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Simin Yu","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044704001","display_name":"Kuntian Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"Osaka University","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kuntian Zhang","raw_affiliation_strings":["Osaka University, Osaka, Japan"],"affiliations":[{"raw_affiliation_string":"Osaka University, Osaka, Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036148682","display_name":"Chuan Xiao","orcid":"https://orcid.org/0000-0001-7239-5134"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"Osaka University","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Chuan Xiao","raw_affiliation_strings":["Osaka University, Osaka, Japan"],"affiliations":[{"raw_affiliation_string":"Osaka University, Osaka, Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085691550","display_name":"Xianyu Bao","orcid":"https://orcid.org/0000-0003-1590-083X"},"institutions":[{"id":"https://openalex.org/I4210097984","display_name":"Shenzhen Academy of Inspection and Quarantine","ror":"https://ror.org/011v81t74","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210097984"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianyu Bao","raw_affiliation_strings":["Shenzhen Academy of Inspection and Quarantine, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Academy of Inspection and Quarantine, Shenzhen, China","institution_ids":["https://openalex.org/I4210097984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003347359","display_name":"Joshua Zhexue Huang","orcid":"https://orcid.org/0000-0002-6797-2571"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Joshua Zhexue Huang","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037374554","display_name":"Mark Junjie Li","orcid":"https://orcid.org/0000-0002-7252-5346"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mark Junjie Li","raw_affiliation_strings":["Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.084,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.311389,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":58,"max":68},"biblio":{"volume":"15","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Image Forgery Detection and Identification","score":0.9983,"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/T12357","display_name":"Digital Image Forgery Detection and Identification","score":0.9983,"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/T11105","display_name":"Single Image Super-Resolution Techniques","score":0.9982,"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/T10775","display_name":"Generative Adversarial Networks in Image Processing","score":0.998,"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/discriminator","display_name":"Discriminator","score":0.90132093},{"id":"https://openalex.org/keywords/generative-adversarial-networks","display_name":"Generative Adversarial Networks","score":0.563426},{"id":"https://openalex.org/keywords/representation-learning","display_name":"Representation Learning","score":0.536067},{"id":"https://openalex.org/keywords/resampling-detection","display_name":"Resampling Detection","score":0.500944},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.49549758}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.90132093},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.8073919},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7456039},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.6780501},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.67398214},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6188648},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.51146305},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.49549758},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43141997},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.41865745},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.41522902},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3788338},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.34717673},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.21378261},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.13179484},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.107356876},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.09501442},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.080177516},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"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/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.76,"display_name":"Reduced inequalities"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":55,"referenced_works":["https://openalex.org/W1834627138","https://openalex.org/W1844684489","https://openalex.org/W1875842236","https://openalex.org/W1959608418","https://openalex.org/W2099471712","https://openalex.org/W2112796928","https://openalex.org/W2183341477","https://openalex.org/W2411541852","https://openalex.org/W2412320034","https://openalex.org/W2521028896","https://openalex.org/W2548275288","https://openalex.org/W2554314924","https://openalex.org/W2573380384","https://openalex.org/W2605195953","https://openalex.org/W2617322972","https://openalex.org/W2617539464","https://openalex.org/W2738588019","https://openalex.org/W2769419222","https://openalex.org/W2785678896","https://openalex.org/W2804330890","https://openalex.org/W2893749619","https://openalex.org/W2911910629","https://openalex.org/W2919933730","https://openalex.org/W2950776302","https://openalex.org/W2952716587","https://openalex.org/W2962706768","https://openalex.org/W2962760235","https://openalex.org/W2962770929","https://openalex.org/W2962793481","https://openalex.org/W2962808998","https://openalex.org/W2962879692","https://openalex.org/W2962892300","https://openalex.org/W2963142510","https://openalex.org/W2963684088","https://openalex.org/W2963775347","https://openalex.org/W2963809789","https://openalex.org/W2963836885","https://openalex.org/W2963931155","https://openalex.org/W2963977471","https://openalex.org/W2963981733","https://openalex.org/W2964167449","https://openalex.org/W2979776030","https://openalex.org/W3009389110","https://openalex.org/W3014080044","https://openalex.org/W3035037798","https://openalex.org/W3099886499","https://openalex.org/W3118608800","https://openalex.org/W4293320219","https://openalex.org/W4293568373","https://openalex.org/W4294568686","https://openalex.org/W4294643831","https://openalex.org/W4295274059","https://openalex.org/W4295521014","https://openalex.org/W4301206121","https://openalex.org/W4320013936"],"related_works":["https://openalex.org/W4308928038","https://openalex.org/W4283822356","https://openalex.org/W4200430540","https://openalex.org/W3217069185","https://openalex.org/W3141413246","https://openalex.org/W3049340819","https://openalex.org/W2366944513","https://openalex.org/W2129146436","https://openalex.org/W2032507829","https://openalex.org/W1950940422"],"abstract_inverted_index":{"TripletGAN":[0,21,136],"is":[1],"a":[2,17,95,109],"variant":[3],"of":[4,14,33,75,77,103,125,131,142],"Generative":[5],"Adversarial":[6],"Network":[7],"(GAN)":[8],"by":[9],"replacing":[10],"the":[11,31,39,51,61,73,98,101,123,133,138],"classification":[12],"loss":[13,36,88],"discriminator":[15],"with":[16,89,116],"triplet":[18,35,87],"loss.":[19],"Although":[20],"delivers":[22],"better":[23],"mode":[24],"coverage":[25],"than":[26],"vanilla":[27],"GAN":[28],"thanks":[29],"to":[30,58,93,113],"characteristics":[32],"adversarial":[34,46],"that":[37,53,147],"maximizes":[38],"embedding":[40],"distance":[41],"between":[42,97],"generated":[43,55,104],"samples,":[44],"its":[45],"training":[47],"method":[48],"suffers":[49],"from":[50,60],"drawback":[52],"some":[54,145],"images":[56,67],"tend":[57],"deviate":[59],"real":[62],"sample":[63],"distribution":[64],"and":[65,100,137,151],"noisy":[66],"are":[68],"produced":[69],"as":[70,154],"we":[71,82],"increase":[72],"number":[74],"iterations":[76],"training.":[78],"In":[79],"this":[80],"paper,":[81],"propose":[83],"an":[84,117],"adversarially":[85],"balanced":[86],"four":[90],"dynamic":[91],"coefficients":[92],"achieve":[94],"trade-off":[96],"quality":[99],"diversity":[102],"samples.":[105],"We":[106],"also":[107],"design":[108],"novel":[110],"network":[111],"architecture":[112],"provide":[114],"GANs":[115],"auto-encoding":[118],"ability.":[119],"Extensive":[120],"experiments":[121],"demonstrate":[122],"effectiveness":[124],"our":[126],"proposed":[127],"methods":[128,146],"in":[129,135,140],"terms":[130,141],"alleviating":[132],"problem":[134],"superiority":[139],"reconstruction":[143],"over":[144],"directly":[148],"train":[149],"generator":[150],"encoder":[152],"such":[153],"O-GAN.":[155]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3200445570","counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2024-10-07T18:22:41.238804","created_date":"2021-09-27"}