{"id":"https://openalex.org/W3089628124","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207478","title":"Stochastic Adversarial Learning for Domain Adaptation","display_name":"Stochastic Adversarial Learning for Domain Adaptation","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3089628124","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207478","mag":"3089628124"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207478","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207478","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5061908942","display_name":"Jen\u2010Tzung Chien","orcid":"https://orcid.org/0000-0003-3466-8941"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jen-Tzung Chien","raw_affiliation_strings":["Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102105977","display_name":"Ching\u2010Wei Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ching-Wei Huang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I148366613"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9995999932289124,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9995999932289124,"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/T12676","display_name":"Machine Learning and ELM","score":0.9527999758720398,"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/discriminative-model","display_name":"Discriminative model","score":0.790165901184082},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7288674116134644},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7046895027160645},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6589925289154053},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6425106525421143},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.6421746015548706},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5761549472808838},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5729080438613892},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4608345627784729},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4359263777732849},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.4341077506542206},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41959989070892334},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4100556969642639},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.17862406373023987},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16951417922973633}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.790165901184082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7288674116134644},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7046895027160645},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6589925289154053},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6425106525421143},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.6421746015548706},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5761549472808838},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5729080438613892},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4608345627784729},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4359263777732849},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.4341077506542206},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41959989070892334},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4100556969642639},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.17862406373023987},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16951417922973633},{"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/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207478","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207478","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W1522301498","https://openalex.org/W1731081199","https://openalex.org/W1956343362","https://openalex.org/W1959608418","https://openalex.org/W1975954951","https://openalex.org/W2002870907","https://openalex.org/W2025854503","https://openalex.org/W2099471712","https://openalex.org/W2108501770","https://openalex.org/W2111362445","https://openalex.org/W2112483442","https://openalex.org/W2115403315","https://openalex.org/W2125865219","https://openalex.org/W2159291411","https://openalex.org/W2163302275","https://openalex.org/W2207593006","https://openalex.org/W2511131004","https://openalex.org/W2617322972","https://openalex.org/W2770645414","https://openalex.org/W2775654873","https://openalex.org/W2786656445","https://openalex.org/W2888473984","https://openalex.org/W2952068867","https://openalex.org/W2953127297","https://openalex.org/W2962750142","https://openalex.org/W2962892300","https://openalex.org/W2963777311","https://openalex.org/W2964109570","https://openalex.org/W2964121744","https://openalex.org/W2973099347","https://openalex.org/W2988016942","https://openalex.org/W2989031637","https://openalex.org/W3007146018","https://openalex.org/W3015301038","https://openalex.org/W4320013936","https://openalex.org/W6600949241","https://openalex.org/W6631190155","https://openalex.org/W6637618735","https://openalex.org/W6640850671","https://openalex.org/W6640963894","https://openalex.org/W6675944832","https://openalex.org/W6676840641","https://openalex.org/W6677069268","https://openalex.org/W6678814708","https://openalex.org/W6683633756","https://openalex.org/W6684149856","https://openalex.org/W6725448924","https://openalex.org/W6737965738","https://openalex.org/W6746789195"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W4246396837","https://openalex.org/W3176240006","https://openalex.org/W3126451824","https://openalex.org/W2482350142","https://openalex.org/W2965546495","https://openalex.org/W1561927205","https://openalex.org/W3008648540","https://openalex.org/W2740361506","https://openalex.org/W4312246223"],"abstract_inverted_index":{"Learning":[0],"across":[1],"domains":[2,77],"is":[3,19,64,105],"challenging":[4],"especially":[5],"when":[6,22],"test":[7],"data":[8,42,48],"in":[9,56],"target":[10,50],"domain":[11,37,45,109,112,141],"are":[12,53,79,91,114],"sparse,":[13],"heterogeneous":[14],"and":[15,46,95],"unlabeled.":[16],"This":[17,29],"challenge":[18],"even":[20],"severe":[21],"building":[23],"a":[24,32,61],"deep":[25],"stochastic":[26,33,139],"neural":[27,103],"model.":[28],"paper":[30],"presents":[31],"semi-supervised":[34],"learning":[35],"for":[36,72,124],"adaptation":[38],"by":[39,81],"using":[40],"labeled":[41],"from":[43,49],"source":[44],"unlabeled":[47],"domain.":[51],"There":[52],"twofold":[54],"novelties":[55],"the":[57,68,87,119,134,137],"proposed":[58,138],"method.":[59],"First,":[60],"graphical":[62],"model":[63,104],"constructed":[65],"to":[66,98,107,117],"identify":[67],"random":[69],"latent":[70],"features":[71,89,113,123],"classes":[73,94],"as":[74,76],"well":[75],"which":[78,90],"learned":[80,116],"variational":[82],"inference.":[83],"Second,":[84],"we":[85],"learn":[86],"class":[88,122],"discriminative":[92],"among":[93],"simultaneously":[96],"invariant":[97],"both":[99],"domains.":[100],"An":[101],"adversarial":[102,140],"introduced":[106],"pursue":[108],"invariance.":[110],"The":[111,128],"explicitly":[115],"purify":[118],"extraction":[120],"of":[121,136],"an":[125],"improved":[126],"classification.":[127],"experiments":[129],"on":[130],"sentiment":[131],"classification":[132],"illustrate":[133],"merits":[135],"adaptation.":[142]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
