{"id":"https://openalex.org/W4401856725","doi":"https://doi.org/10.1145/3637528.3671914","title":"Geometric View of Soft Decorrelation in Self-Supervised Learning","display_name":"Geometric View of Soft Decorrelation in Self-Supervised Learning","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401856725","doi":"https://doi.org/10.1145/3637528.3671914"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671914","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671914","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671914","source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671914","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100386948","display_name":"Yifei Zhang","orcid":"https://orcid.org/0000-0003-4185-8663"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Yifei Zhang","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0003-4185-8663","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101324723","display_name":"Hao Zhu","orcid":"https://orcid.org/0000-0002-1885-8051"},"institutions":[{"id":"https://openalex.org/I1292875679","display_name":"Commonwealth Scientific and Industrial Research Organisation","ror":"https://ror.org/03qn8fb07","country_code":"AU","type":"government","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hao Zhu","raw_affiliation_strings":["CSIRO, Sydney, Australia"],"raw_orcid":"https://orcid.org/0000-0002-1885-8051","affiliations":[{"raw_affiliation_string":"CSIRO, Sydney, Australia","institution_ids":["https://openalex.org/I1292875679"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036555049","display_name":"Zixing Song","orcid":"https://orcid.org/0000-0002-8871-3990"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zixing Song","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0002-8871-3990","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053712210","display_name":"Yankai Chen","orcid":"https://orcid.org/0000-0001-5741-2047"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yankai Chen","raw_affiliation_strings":["Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0001-5741-2047","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101478439","display_name":"Xinyu Fu","orcid":"https://orcid.org/0000-0002-7195-2549"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xinyu Fu","raw_affiliation_strings":["Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0002-7195-2549","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027386336","display_name":"Ziqiao Meng","orcid":"https://orcid.org/0000-0002-0231-5992"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Ziqiao Meng","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0002-0231-5992","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002212263","display_name":"Piotr Koniusz","orcid":"https://orcid.org/0000-0002-6340-5289"},"institutions":[{"id":"https://openalex.org/I1292875679","display_name":"Commonwealth Scientific and Industrial Research Organisation","ror":"https://ror.org/03qn8fb07","country_code":"AU","type":"government","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I4387156119"]},{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Piotr Koniusz","raw_affiliation_strings":["Data61, CSIRO, Canberra, Australia"],"raw_orcid":"https://orcid.org/0000-0002-6340-5289","affiliations":[{"raw_affiliation_string":"Data61, CSIRO, Canberra, Australia","institution_ids":["https://openalex.org/I42894916","https://openalex.org/I1292875679"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042251906","display_name":"Irwin King","orcid":"https://orcid.org/0000-0001-8106-6447"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Irwin King","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0001-8106-6447","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5100386948"],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":null,"apc_paid":null,"fwci":4.967,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.95779428,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"4338","last_page":"4349"},"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":0.9743000268936157,"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":0.9743000268936157,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9259999990463257,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9041000008583069,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/decorrelation","display_name":"Decorrelation","score":0.9145504236221313},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6637719869613647},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5474231839179993},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3876269459724426}],"concepts":[{"id":"https://openalex.org/C177860922","wikidata":"https://www.wikidata.org/wiki/Q788608","display_name":"Decorrelation","level":2,"score":0.9145504236221313},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6637719869613647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5474231839179993},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3876269459724426}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3637528.3671914","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671914","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671914","source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:openresearch-repository.anu.edu.au:1885/733752413","is_oa":false,"landing_page_url":"http://www.scopus.com/inward/record.url?scp=85203674870&partnerID=8YFLogxK","pdf_url":null,"source":{"id":"https://openalex.org/S4306402539","display_name":"ANU Open Research (Australian National University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I118347636","host_organization_name":"Australian National University","host_organization_lineage":["https://openalex.org/I118347636"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Conference paper"},{"id":"pmh:oai:research-information.bris.ac.uk:openaire/14633e18-ab87-476d-b09a-bbd780a22e56","is_oa":true,"landing_page_url":"https://hdl.handle.net/1983/14633e18-ab87-476d-b09a-bbd780a22e56","pdf_url":null,"source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Zhang, Y, Zhu, H, Song, Z, Chen, Y, Fu, X, Koniusz, P & King, I 2024, Geometric View of Soft Decorrelation in Self-Supervised Learning. in KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, pp. 4338-4349. https://doi.org/10.1145/3637528.3671914","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:research-information.bris.ac.uk:openaire_cris_publications/14633e18-ab87-476d-b09a-bbd780a22e56","is_oa":true,"landing_page_url":"https://research-information.bris.ac.uk/en/publications/14633e18-ab87-476d-b09a-bbd780a22e56","pdf_url":null,"source":{"id":"https://openalex.org/S4306400895","display_name":"Bristol Research (University of Bristol)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I36234482","host_organization_name":"University of Bristol","host_organization_lineage":["https://openalex.org/I36234482"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Zhang, Y, Zhu, H, Song, Z, Chen, Y, Fu, X, Koniusz, P & King, I 2024, Geometric View of Soft Decorrelation in Self-Supervised Learning. in KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, pp. 4338-4349. https://doi.org/10.1145/3637528.3671914","raw_type":"contributionToPeriodical"}],"best_oa_location":{"id":"doi:10.1145/3637528.3671914","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671914","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671914","source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4401856725.pdf"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W343636949","https://openalex.org/W2027731328","https://openalex.org/W2055597244","https://openalex.org/W2097939965","https://openalex.org/W2108598243","https://openalex.org/W2560674852","https://openalex.org/W2747329762","https://openalex.org/W2963420272","https://openalex.org/W2990251202","https://openalex.org/W3015817662","https://openalex.org/W3035524453","https://openalex.org/W3116954826","https://openalex.org/W3168925038","https://openalex.org/W3171007011","https://openalex.org/W3175593095","https://openalex.org/W3188717957","https://openalex.org/W3193733053","https://openalex.org/W3209785184","https://openalex.org/W3210978902","https://openalex.org/W4212774754","https://openalex.org/W4214689175","https://openalex.org/W4220689021","https://openalex.org/W4281613206","https://openalex.org/W4287028988","https://openalex.org/W4290874961","https://openalex.org/W4290878031","https://openalex.org/W4301793249","https://openalex.org/W4312804044","https://openalex.org/W4362598119","https://openalex.org/W4382202568","https://openalex.org/W4382318032","https://openalex.org/W4385212602","https://openalex.org/W4385541843","https://openalex.org/W4385568031","https://openalex.org/W4387097003","https://openalex.org/W4393147760","https://openalex.org/W4393159476","https://openalex.org/W6600018615","https://openalex.org/W6600319451","https://openalex.org/W6677978660"],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Contrastive":[0],"learning,":[1],"a":[2,15,28,46,93,115,141,157,166],"form":[3],"of":[4,10,27,75,143,177],"Self-Supervised":[5],"Learning":[6],"(SSL),":[7],"typically":[8],"consists":[9],"an":[11,110],"alignment":[12,19],"term":[13,20,34,68],"and":[14,38,88,109,195,206],"regularization":[16,33,49,100],"term.":[17],"The":[18],"minimizes":[21],"the":[22,25,32,43,66,73,97,103,106,119,129,135,147,153,175,192],"distance":[23,104,121],"between":[24,105,122],"embeddings":[26],"positive":[29,123],"pair,":[30],"while":[31],"prevents":[35],"trivial":[36,63],"solutions":[37],"expresses":[39],"prior":[40],"beliefs":[41],"about":[42],"embeddings.":[44],"As":[45],"widely":[47],"used":[48],"technique,":[50],"soft":[51,98,130,154,193],"decorrelation":[52,67,99,131,155,194],"has":[53],"been":[54],"employed":[55],"by":[56,201],"several":[57],"non-contrastive":[58],"SSL":[59],"methods":[60],"to":[61,71,83,101,126,191],"avoid":[62],"solutions.":[64],"While":[65],"is":[69],"designed":[70],"address":[72],"issue":[74,176],"dimensional":[76,136,178],"collapse,":[77],"we":[78,95,139],"find":[79],"that":[80,165],"it":[81],"fails":[82],"achieve":[84],"this":[85,161,171],"goal":[86],"theoretically":[87],"experimentally.":[89],"Based":[90],"on":[91,118,185,204],"such":[92],"finding,":[94],"extend":[96],"minimize":[102],"covariance":[107],"matrix":[108],"identity":[111],"matrix.":[112],"We":[113,163],"provide":[114],"new":[116],"perspective":[117],"geometric":[120],"definite":[124],"matrices":[125],"investigate":[127],"why":[128],"cannot":[132],"efficiently":[133],"solve":[134,174],"collapse.":[137,179],"Furthermore,":[138],"construct":[140],"family":[142,172],"loss":[144,167,182],"functions":[145,183],"utilizing":[146],"Bregman":[148],"Matrix":[149],"Divergence":[150],"(BMD),":[151],"with":[152],"representing":[156],"specific":[158],"instance":[159],"within":[160],"family.":[162],"prove":[164],"function":[168],"(LogDet)":[169],"in":[170],"can":[173],"Our":[180],"novel":[181],"based":[184],"BMD":[186],"exhibit":[187],"superior":[188],"performance":[189],"compared":[190],"other":[196],"baseline":[197],"techniques,":[198],"as":[199],"demonstrated":[200],"experimental":[202],"results":[203],"graph":[205],"image":[207],"datasets.":[208]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}