{"id":"https://openalex.org/W4392904181","doi":"https://doi.org/10.1109/icassp48485.2024.10448096","title":"Uncertainty-Guided Contrastive Learning For Single Source Domain Generalisation","display_name":"Uncertainty-Guided Contrastive Learning For Single Source Domain Generalisation","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392904181","doi":"https://doi.org/10.1109/icassp48485.2024.10448096"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10448096","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10448096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/97302/2/Kollias%20Uncertainty-Guided%20Contrastive%20Learning%20For%20Single%20Source%20Domain%20Generalisation%202024%20Accepted.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016292531","display_name":"\u0391\u03bd\u03b1\u03c3\u03c4\u03ac\u03c3\u03b9\u03bf\u03c2 \u0391\u03c1\u03c3\u03ad\u03bd\u03bf\u03c2","orcid":"https://orcid.org/0009-0000-0332-6194"},"institutions":[{"id":"https://openalex.org/I174458059","display_name":"National Technical University of Athens","ror":"https://ror.org/03cx6bg69","country_code":"GR","type":"education","lineage":["https://openalex.org/I174458059"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Anastasios Arsenos","raw_affiliation_strings":["National Technical University of Athens,School of Electrical & Computer Engineering,Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Technical University of Athens,School of Electrical & Computer Engineering,Greece","institution_ids":["https://openalex.org/I174458059"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029879679","display_name":"Dimitrios Kollias","orcid":"https://orcid.org/0000-0002-8188-3751"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Dimitrios Kollias","raw_affiliation_strings":["Queen Mary University of London,School of Electronic Engineering & Computer Science,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Queen Mary University of London,School of Electronic Engineering & Computer Science,UK","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048723394","display_name":"Evangelos Petrongonas","orcid":"https://orcid.org/0000-0002-4689-7509"},"institutions":[{"id":"https://openalex.org/I174458059","display_name":"National Technical University of Athens","ror":"https://ror.org/03cx6bg69","country_code":"GR","type":"education","lineage":["https://openalex.org/I174458059"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Evangelos Petrongonas","raw_affiliation_strings":["National Technical University of Athens,School of Electrical & Computer Engineering,Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Technical University of Athens,School of Electrical & Computer Engineering,Greece","institution_ids":["https://openalex.org/I174458059"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064386485","display_name":"Christos Skliros","orcid":"https://orcid.org/0000-0001-9406-9655"},"institutions":[{"id":"https://openalex.org/I94353236","display_name":"Hellenic Center for Disease Control & Prevention","ror":"https://ror.org/055t7f808","country_code":"GR","type":"other","lineage":["https://openalex.org/I94353236"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Christos Skliros","raw_affiliation_strings":["Hellenic Drones S.A.,Greece","Hellenic Drones S.A., Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hellenic Drones S.A.,Greece","institution_ids":["https://openalex.org/I94353236"]},{"raw_affiliation_string":"Hellenic Drones S.A., Greece","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001457450","display_name":"Stefanos Kollias","orcid":"https://orcid.org/0000-0003-2899-0598"},"institutions":[{"id":"https://openalex.org/I174458059","display_name":"National Technical University of Athens","ror":"https://ror.org/03cx6bg69","country_code":"GR","type":"education","lineage":["https://openalex.org/I174458059"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Stefanos Kollias","raw_affiliation_strings":["National Technical University of Athens,School of Electrical & Computer Engineering,Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Technical University of Athens,School of Electrical & Computer Engineering,Greece","institution_ids":["https://openalex.org/I174458059"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.778,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.85916014,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"6935","last_page":"6939"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9905999898910522,"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/T10515","display_name":"Cancer-related molecular mechanisms research","score":0.9476000070571899,"subfield":{"id":"https://openalex.org/subfields/1306","display_name":"Cancer Research"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7429768443107605},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6551061272621155},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6191992163658142},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5936251282691956},{"id":"https://openalex.org/keywords/subnetwork","display_name":"Subnetwork","score":0.5850927829742432},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5091201663017273},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.47832250595092773},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.4680808484554291},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43031638860702515},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.42759770154953003},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4209405779838562},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3708455562591553},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3684021830558777},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3481956720352173},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.17210769653320312},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17129024863243103},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.1656762659549713}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7429768443107605},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6551061272621155},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6191992163658142},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5936251282691956},{"id":"https://openalex.org/C2780186347","wikidata":"https://www.wikidata.org/wiki/Q11414","display_name":"Subnetwork","level":2,"score":0.5850927829742432},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5091201663017273},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.47832250595092773},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.4680808484554291},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43031638860702515},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.42759770154953003},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4209405779838562},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3708455562591553},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3684021830558777},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3481956720352173},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.17210769653320312},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17129024863243103},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.1656762659549713},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icassp48485.2024.10448096","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10448096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/97302","is_oa":true,"landing_page_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/97302","pdf_url":"https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/97302/2/Kollias%20Uncertainty-Guided%20Contrastive%20Learning%20For%20Single%20Source%20Domain%20Generalisation%202024%20Accepted.pdf","source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"}],"best_oa_location":{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/97302","is_oa":true,"landing_page_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/97302","pdf_url":"https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/97302/2/Kollias%20Uncertainty-Guided%20Contrastive%20Learning%20For%20Single%20Source%20Domain%20Generalisation%202024%20Accepted.pdf","source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4392904181.pdf"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W2008790887","https://openalex.org/W2127757833","https://openalex.org/W2763549966","https://openalex.org/W2806187986","https://openalex.org/W2842511635","https://openalex.org/W2958360136","https://openalex.org/W2964137095","https://openalex.org/W3034373371","https://openalex.org/W3128475431","https://openalex.org/W3171007011","https://openalex.org/W3175146247","https://openalex.org/W3175956495","https://openalex.org/W3190457880","https://openalex.org/W4213414377","https://openalex.org/W4214480085","https://openalex.org/W4288404646","https://openalex.org/W4297808394","https://openalex.org/W4312365264","https://openalex.org/W4312823897","https://openalex.org/W4319300162","https://openalex.org/W4375839990","https://openalex.org/W6684488266","https://openalex.org/W6752160200","https://openalex.org/W6757555829","https://openalex.org/W6781506369"],"related_works":["https://openalex.org/W2060724872","https://openalex.org/W2082094785","https://openalex.org/W2202198356","https://openalex.org/W3087203342","https://openalex.org/W2377184161","https://openalex.org/W228984114","https://openalex.org/W4226360758","https://openalex.org/W2151093953","https://openalex.org/W2090026684","https://openalex.org/W2907567977"],"abstract_inverted_index":{"In":[0,33],"the":[1,7,56,66,73,94,101,125],"context":[2],"of":[3,77,96],"single":[4,21,121],"domain":[5,22,68,74],"generalisation,":[6],"objective":[8],"is":[9,53],"for":[10],"models":[11],"that":[12],"have":[13],"been":[14],"exclusively":[15],"trained":[16],"on":[17,85],"data":[18],"from":[19,119],"a":[20,38,120],"to":[23,42,54,107],"demonstrate":[24,93],"strong":[25],"performance":[26],"when":[27],"confronted":[28],"with":[29],"various":[30],"unfamiliar":[31],"domains.":[32],"this":[34],"paper,":[35],"we":[36],"introduce":[37],"novel":[39],"model":[40],"referred":[41],"as":[43],"Contrastive":[44],"Uncertainty":[45],"Domain":[46,89],"Generalisation":[47,90],"Network":[48],"(CUDGNet).":[49],"The":[50],"key":[51],"idea":[52],"augment":[55],"source":[57],"capacity":[58],"in":[59],"both":[60],"input":[61],"and":[62,70],"label":[63],"spaces":[64],"through":[65,80,124],"fictitious":[67],"generator":[69,126],"jointly":[71],"learn":[72],"invariant":[75],"representation":[76],"each":[78],"class":[79],"contrastive":[81],"learning.":[82],"Extensive":[83],"experiments":[84],"two":[86],"Single":[87],"Source":[88],"(SSDG)":[91],"datasets":[92],"effectiveness":[95],"our":[97],"approach,":[98],"which":[99],"surpasses":[100],"state-of-the-art":[102],"single-DG":[103],"methods":[104],"by":[105],"up":[106],"7.08%.":[108],"Our":[109],"method":[110],"also":[111],"provides":[112],"efficient":[113],"uncertainty":[114],"estimation":[115],"at":[116],"inference":[117],"time":[118],"forward":[122],"pass":[123],"subnetwork.":[127]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}