{"id":"https://openalex.org/W7163553917","doi":"https://doi.org/10.48550/arxiv.2606.05109","title":"RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities","display_name":"RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities","publication_year":2026,"publication_date":"2026-06-03","ids":{"openalex":"https://openalex.org/W7163553917","doi":"https://doi.org/10.48550/arxiv.2606.05109"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.05109","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05109","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.05109","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137900847","display_name":"Vasiliki Rizou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rizou, Vasiliki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000947076","display_name":"Pascal Frossard","orcid":"https://orcid.org/0000-0002-4010-714X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Frossard, Pascal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5072466998","display_name":"Dorina Thanou","orcid":"https://orcid.org/0000-0003-2319-4832"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thanou, Dorina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.6833000183105469,"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":0.6833000183105469,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.15649999678134918,"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":0.0471000000834465,"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/exploit","display_name":"Exploit","score":0.8051999807357788},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.7139999866485596},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6712999939918518},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6406000256538391},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5296000242233276},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5134999752044678},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.45739999413490295}],"concepts":[{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.8051999807357788},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7663999795913696},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.7139999866485596},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6712999939918518},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6406000256538391},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5960999727249146},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5378000140190125},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5296000242233276},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5134999752044678},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.45739999413490295},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35679998993873596},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.35530000925064087},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.33399999141693115},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.290800005197525},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2563000023365021}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.05109","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05109","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.05109","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05109","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"To":[0,73],"leverage":[1],"the":[2,14,64,108,119,136,148],"full":[3],"potential":[4],"of":[5,150],"multimodal":[6,52,97],"data,":[7],"we":[8,76,127],"need":[9,109],"representations":[10,31],"that":[11,44,146],"go":[12],"beyond":[13,92],"state-of-the-art":[15],"alignment":[16],"and":[17,20,41,87,138,141,156,167],"fusion":[18],"approaches":[19],"exploit":[21],"all":[22],"cross-modal":[23],"interactions":[24],"without":[25],"sacrificing":[26],"modality-specific":[27],"information.":[28],"Learning":[29],"disentangled":[30,161],"is":[32,54],"a":[33,55,79,96,129],"principled":[34],"way":[35],"to":[36,63,68,83],"identify":[37],"these":[38,85],"underlying":[39,120],"shared":[40,137],"unique":[42,139],"factors":[43],"are":[45,60],"hidden":[46],"in":[47],"observational":[48],"data.":[49],"However,":[50],"while":[51,114,163],"disentanglement":[53,91],"compelling":[56],"paradigm,":[57],"existing":[58],"methods":[59],"largely":[61],"confined":[62],"two-modality":[65],"regime":[66],"due":[67],"its":[69],"inherent":[70],"scalability":[71],"bottleneck.":[72],"address":[74],"this,":[75],"propose":[77],"RePercENT,":[78],"self-supervised":[80],"framework":[81],"designed":[82],"surpass":[84],"limitations":[86],"unlocks":[88],"scalable":[89],"pairwise":[90],"two":[93],"modalities.":[94],"Through":[95],"`plug-and-play'":[98],"architecture,":[99],"our":[100,151],"approach":[101],"operates":[102],"directly":[103],"on":[104],"pre-extracted":[105],"embeddings,":[106],"eliminating":[107],"for":[110,133],"extensive":[111],"joint":[112,130],"pre-training":[113],"making":[115],"no":[116],"assumptions":[117],"regarding":[118],"modalities":[121,155],"or":[122],"foundation":[123],"model":[124],"backbones.":[125],"Moreover,":[126],"introduce":[128],"optimization":[131],"objective":[132],"simultaneously":[134],"deriving":[135],"components,":[140],"provide":[142],"formal":[143],"theoretical":[144],"guarantees":[145],"characterize":[147],"optimality":[149],"solution.":[152],"Across":[153],"diverse":[154],"tasks,":[157],"RePercENT":[158],"successfully":[159],"recovers":[160],"components":[162],"maintaining":[164],"competitive":[165],"performance":[166],"significantly":[168],"reducing":[169],"computational":[170],"complexity.":[171]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-05T00:00:00"}
