{"id":"https://openalex.org/W4417237993","doi":"https://doi.org/10.1109/smc58881.2025.11342641","title":"Sliced Wasserstein Discrepancy in Disentangling Representation and Adaptation Networks for Unsupervised Domain Adaptation","display_name":"Sliced Wasserstein Discrepancy in Disentangling Representation and Adaptation Networks for Unsupervised Domain Adaptation","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W4417237993","doi":"https://doi.org/10.1109/smc58881.2025.11342641"},"language":"en","primary_location":{"id":"doi:10.1109/smc58881.2025.11342641","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342641","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2504.03043","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5098901798","display_name":"Joel Sol","orcid":null},"institutions":[{"id":"https://openalex.org/I212119943","display_name":"University of Victoria","ror":"https://ror.org/04s5mat29","country_code":"CA","type":"education","lineage":["https://openalex.org/I212119943"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Joel Sol","raw_affiliation_strings":["University of Victoria,Faculty of Engineering and Computer Science,Victoria,Canada,V8P 5C2"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Victoria,Faculty of Engineering and Computer Science,Victoria,Canada,V8P 5C2","institution_ids":["https://openalex.org/I212119943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060960234","display_name":"Shadi Alijani","orcid":null},"institutions":[{"id":"https://openalex.org/I212119943","display_name":"University of Victoria","ror":"https://ror.org/04s5mat29","country_code":"CA","type":"education","lineage":["https://openalex.org/I212119943"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Shadi Alijani","raw_affiliation_strings":["University of Victoria,Faculty of Engineering and Computer Science,Victoria,Canada,V8P 5C2"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Victoria,Faculty of Engineering and Computer Science,Victoria,Canada,V8P 5C2","institution_ids":["https://openalex.org/I212119943"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058540009","display_name":"Homayoun Najjaran","orcid":"https://orcid.org/0000-0002-3550-225X"},"institutions":[{"id":"https://openalex.org/I212119943","display_name":"University of Victoria","ror":"https://ror.org/04s5mat29","country_code":"CA","type":"education","lineage":["https://openalex.org/I212119943"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Homayoun Najjaran","raw_affiliation_strings":["University of Victoria,Faculty of Engineering and Computer Science,Victoria,Canada,V8P 5C2"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Victoria,Faculty of Engineering and Computer Science,Victoria,Canada,V8P 5C2","institution_ids":["https://openalex.org/I212119943"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I212119943"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3685589,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6455","last_page":"6460"},"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":0.6797999739646912,"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":0.6797999739646912,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.19740000367164612,"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/T11448","display_name":"Face recognition and analysis","score":0.0414000004529953,"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/domain-adaptation","display_name":"Domain adaptation","score":0.7172999978065491},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6987000107765198},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6424999833106995},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5812000036239624},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5741000175476074},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5396000146865845},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.48809999227523804},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.46650001406669617}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7243000268936157},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.7172999978065491},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6987000107765198},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6424999833106995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6373000144958496},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5812000036239624},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5741000175476074},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5396000146865845},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.48809999227523804},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.46650001406669617},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3472000062465668},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.34630000591278076},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3416999876499176},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C77246614","wikidata":"https://www.wikidata.org/wiki/Q1409400","display_name":"Gramian matrix","level":3,"score":0.3165000081062317},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26829999685287476},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2549000084400177}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/smc58881.2025.11342641","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342641","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2504.03043","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.03043","pdf_url":"https://arxiv.org/pdf/2504.03043","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:doi:10.48550/arxiv.2504.03043","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2504.03043","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2504.03043","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2504.03043","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.03043","pdf_url":"https://arxiv.org/pdf/2504.03043","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2773004715","https://openalex.org/W3166542014","https://openalex.org/W2475287302","https://openalex.org/W2980096013","https://openalex.org/W2112796928","https://openalex.org/W2100659887","https://openalex.org/W2487365028","https://openalex.org/W2340897893","https://openalex.org/W4401808768","https://openalex.org/W2104094955","https://openalex.org/W2964288524","https://openalex.org/W2593768305","https://openalex.org/W2478454054","https://openalex.org/W4393321715","https://openalex.org/W4393316736","https://openalex.org/W4392902986","https://openalex.org/W2603777577","https://openalex.org/W3002450089","https://openalex.org/W3179365255","https://openalex.org/W2331128040","https://openalex.org/W2962850830"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"DRANet-SWD":[3,78],"as":[4,34],"a":[5,35,86],"novel":[6],"complete":[7],"pipeline":[8],"for":[9,17,55],"disentangling":[10],"content":[11],"and":[12,69,110],"style":[13,36,57,97],"representations":[14],"of":[15,39,48,91,104],"images":[16],"unsupervised":[18],"domain":[19,60,112],"adaptation":[20,61,113],"(UDA).":[21],"The":[22,45],"approach":[23],"builds":[24],"upon":[25],"DRANet":[26],"by":[27],"incorporating":[28],"the":[29,40,51,74,102],"sliced":[30],"Wasserstein":[31],"discrepancy":[32],"(SWD)":[33],"loss":[37,54],"instead":[38],"traditional":[41],"Gram":[42,52],"matrix":[43,53],"loss.":[44],"potential":[46],"advantages":[47],"SWD":[49,84,105],"over":[50],"capturing":[56],"variations":[58],"in":[59,106],"are":[62],"investigated.":[63],"Experiments":[64],"using":[65],"digit":[66],"classification":[67],"datasets":[68],"driving":[70],"scenario":[71],"segmentation":[72],"validate":[73],"method,":[75],"demonstrating":[76],"that":[77,83],"enhances":[79],"performance.":[80],"Results":[81],"indicate":[82],"provides":[85],"more":[87],"robust":[88],"statistical":[89],"comparison":[90],"feature":[92,108],"distributions,":[93],"leading":[94],"to":[95],"better":[96],"adaptation.":[98],"These":[99],"findings":[100],"highlight":[101],"effectiveness":[103],"refining":[107],"alignment":[109],"improving":[111],"tasks":[114],"across":[115],"these":[116],"benchmarks.":[117],"Our":[118],"code":[119],"can":[120],"be":[121],"found":[122],"here.":[123]},"counts_by_year":[],"updated_date":"2026-07-01T08:55:40.977307","created_date":"2025-10-10T00:00:00"}