{"id":"https://openalex.org/W4408963113","doi":"https://doi.org/10.1007/s10994-025-06760-x","title":"An unsupervised adversarial domain adaptation based on variational auto-encoder","display_name":"An unsupervised adversarial domain adaptation based on variational auto-encoder","publication_year":2025,"publication_date":"2025-03-26","ids":{"openalex":"https://openalex.org/W4408963113","doi":"https://doi.org/10.1007/s10994-025-06760-x"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-025-06760-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06760-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06760-x.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06760-x.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042256436","display_name":"Mahta Hassan Pour Zonoozi","orcid":null},"institutions":[{"id":"https://openalex.org/I136830121","display_name":"Islamic Azad University South Tehran Branch","ror":"https://ror.org/02xc21a77","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433","https://openalex.org/I136830121"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mahta Hassan Pour Zonoozi","raw_affiliation_strings":["Faculty of Technical and Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Technical and Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran","institution_ids":["https://openalex.org/I136830121"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030892757","display_name":"Vahid Seydi","orcid":"https://orcid.org/0000-0001-5702-2209"},"institutions":[{"id":"https://openalex.org/I136830121","display_name":"Islamic Azad University South Tehran Branch","ror":"https://ror.org/02xc21a77","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433","https://openalex.org/I136830121"]},{"id":"https://openalex.org/I161548249","display_name":"Bangor University","ror":"https://ror.org/006jb1a24","country_code":"GB","type":"education","lineage":["https://openalex.org/I161548249"]}],"countries":["GB","IR"],"is_corresponding":true,"raw_author_name":"Vahid Seydi","raw_affiliation_strings":["Centre for Applied Marine Sciences, School of Ocean Sciences, Bangor University, Menai Bridge, UK, Bangor, UK","Faculty of Technical and Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Applied Marine Sciences, School of Ocean Sciences, Bangor University, Menai Bridge, UK, Bangor, UK","institution_ids":["https://openalex.org/I161548249"]},{"raw_affiliation_string":"Faculty of Technical and Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran","institution_ids":["https://openalex.org/I136830121"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016214637","display_name":"Mahmood Deypir","orcid":"https://orcid.org/0000-0002-9417-9018"},"institutions":[{"id":"https://openalex.org/I136830121","display_name":"Islamic Azad University South Tehran Branch","ror":"https://ror.org/02xc21a77","country_code":"IR","type":"education","lineage":["https://openalex.org/I110525433","https://openalex.org/I136830121"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mahmood Deypir","raw_affiliation_strings":["Faculty of Technical and Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Technical and Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran","institution_ids":["https://openalex.org/I136830121"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5030892757"],"corresponding_institution_ids":["https://openalex.org/I136830121","https://openalex.org/I161548249"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":1.7312,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.85672811,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"114","issue":"5","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9958999752998352,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9958999752998352,"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":0.984000027179718,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9735000133514404,"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/adversarial-system","display_name":"Adversarial system","score":0.7876748442649841},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6755555868148804},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.6514462232589722},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6120350360870361},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.582676351070404},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5670759081840515},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5509459376335144},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5268341898918152},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4098415970802307},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35709622502326965},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30047911405563354},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.13320651650428772},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.10529235005378723}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7876748442649841},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6755555868148804},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.6514462232589722},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6120350360870361},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.582676351070404},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5670759081840515},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5509459376335144},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5268341898918152},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4098415970802307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35709622502326965},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30047911405563354},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.13320651650428772},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.10529235005378723},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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":[{"id":"doi:10.1007/s10994-025-06760-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06760-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06760-x.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10994-025-06760-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06760-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06760-x.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4408963113.pdf"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W123476658","https://openalex.org/W146900863","https://openalex.org/W1665214252","https://openalex.org/W1731081199","https://openalex.org/W1909320841","https://openalex.org/W2031342017","https://openalex.org/W2095705004","https://openalex.org/W2112796928","https://openalex.org/W2155541015","https://openalex.org/W2157475639","https://openalex.org/W2159291411","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2225156818","https://openalex.org/W2579855779","https://openalex.org/W2584009249","https://openalex.org/W2593768305","https://openalex.org/W2605488490","https://openalex.org/W2786808285","https://openalex.org/W2788768841","https://openalex.org/W2798453135","https://openalex.org/W2893630558","https://openalex.org/W2947356012","https://openalex.org/W2963073614","https://openalex.org/W2964057616","https://openalex.org/W2966145398","https://openalex.org/W2986915914","https://openalex.org/W2998043922","https://openalex.org/W2998666297","https://openalex.org/W3013167959","https://openalex.org/W3093100255","https://openalex.org/W3096831136","https://openalex.org/W3159462103","https://openalex.org/W3168650020","https://openalex.org/W4240805545","https://openalex.org/W4291221223","https://openalex.org/W4292119925","https://openalex.org/W4310124887","https://openalex.org/W4319299822","https://openalex.org/W4320015765","https://openalex.org/W4377000439","https://openalex.org/W4381059462","https://openalex.org/W4385570687","https://openalex.org/W4388097704","https://openalex.org/W6639835855","https://openalex.org/W6640963894","https://openalex.org/W6720691552","https://openalex.org/W6725448924","https://openalex.org/W6750109254","https://openalex.org/W6761334744"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W2502115930","https://openalex.org/W2803255133","https://openalex.org/W4297051394","https://openalex.org/W3034688404","https://openalex.org/W4394775207"],"abstract_inverted_index":{"Abstract":[0],"Collecting":[1],"a":[2,23,52,60,91,114,135,139,153,158,196],"large":[3],"amount":[4],"of":[5,205,215],"labeled":[6,61],"data":[7,28,133,149],"in":[8,27,83,105,199,219],"machine":[9],"learning":[10],"is":[11,49,103,119,182,209,237],"always":[12],"challenging.":[13],"Often,":[14],"even":[15],"with":[16,74,171],"sufficient":[17],"data,":[18],"domain":[19,67,109,202,220,225],"differences":[20],"can":[21],"cause":[22],"shift":[24],"or":[25,236],"bias":[26],"distribution,":[29],"affecting":[30],"model":[31,234],"performance":[32],"during":[33],"testing.":[34],"Domain":[35],"adaptation":[36,110,226],"methods,":[37],"especially":[38],"adversarial":[39,108,168],"techniques,":[40],"are":[41,150],"effective":[42],"solutions":[43],"for":[44,54],"these":[45],"challenges.":[46],"The":[47,162],"goal":[48],"to":[50,66,97,121,137,174,211,239],"learn":[51,175],"classifier":[53],"an":[55,106,167],"unlabeled":[56],"target":[57,132,148,190],"dataset":[58],"using":[59],"source":[62,130,146,187],"dataset,":[63],"enhancing":[64],"resistance":[65],"shifts.":[68],"However,":[69],"existing":[70],"methods":[71],"sometimes":[72],"struggle":[73],"adapting":[75],"the":[76,172,179,185,201,206,213,216],"joint":[77],"feature":[78,126,163,180],"distribution":[79],"across":[80],"domains,":[81],"resulting":[82],"negative":[84,99],"transfer.":[85,100],"To":[86],"address":[87],"this,":[88],"we":[89,194],"propose":[90],"method":[92,102],"that":[93,231],"forms":[94],"class-specific":[95],"clusters":[96],"prevent":[98],"This":[101],"encapsulated":[104],"unsupervised":[107,224],"framework":[111],"based":[112],"on":[113],"variational":[115,154,217],"auto-encoder.":[116],"Our":[117],"structure":[118,198,208],"designed":[120],"enhance":[122],"invariant":[123],"and":[124,131,147,188],"discriminative":[125],"representation.":[127,142,161],"We":[128],"process":[129],"through":[134],"VAE":[136],"establish":[138],"smooth":[140,159],"latent":[141,160],"In":[143,192],"our":[144,228,232],"method,":[145],"fed":[151],"into":[152],"auto-encoder,":[155],"which":[156,200],"produces":[157],"extractor":[164,181],"then":[165],"plays":[166],"minimax":[169],"game":[170],"discriminator":[173,203],"domain-invariant":[176],"features,":[177],"while":[178],"shared":[183],"between":[184],"reconstructed":[186,189],"data.":[191],"addition,":[193],"proposed":[195,233],"second":[197],"part":[204],"prior":[207],"eliminated":[210],"demonstrate":[212],"influence":[214],"auto-encoder":[218],"adaptation.":[221],"On":[222],"numerous":[223],"benchmarks,":[227],"results":[229],"indicate":[230],"outperforms":[235],"comparable":[238],"state-of-the-art":[240],"outcomes.":[241]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}
