{"id":"https://openalex.org/W4378713423","doi":"https://doi.org/10.48550/arxiv.2305.16562","title":"Unsupervised Embedding Quality Evaluation","display_name":"Unsupervised Embedding Quality Evaluation","publication_year":2023,"publication_date":"2023-05-26","ids":{"openalex":"https://openalex.org/W4378713423","doi":"https://doi.org/10.48550/arxiv.2305.16562"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2305.16562","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.16562","pdf_url":"https://arxiv.org/pdf/2305.16562","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2305.16562","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032806916","display_name":"Anton Tsitsulin","orcid":"https://orcid.org/0000-0001-5519-7961","observed_orcids":["https://orcid.org/0000-0001-5519-7961"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tsitsulin, Anton","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078804592","display_name":"Marina Munkhoeva","orcid":"https://orcid.org/0000-0002-5638-3712","observed_orcids":["https://orcid.org/0000-0002-5638-3712"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Munkhoeva, Marina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5005058150","display_name":"Bryan Perozzi","orcid":"https://orcid.org/0009-0002-1639-2056","observed_orcids":["https://orcid.org/0009-0002-1639-2056"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Perozzi, Bryan","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":true,"cited_by_count":2,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.5806000232696533,"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":0.5806000232696533,"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/T10057","display_name":"Face and Expression Recognition","score":0.12600000202655792,"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/T10028","display_name":"Topic Modeling","score":0.08219999819993973,"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/self-supervised-learning","display_name":"self-supervised learning","score":0.9240000247955322},{"id":"https://openalex.org/keywords/unsupervised-evaluation","display_name":"unsupervised evaluation","score":0.5910000205039978},{"id":"https://openalex.org/keywords/representation-learning","display_name":"representation learning","score":0.6740000247955322},{"id":"https://openalex.org/keywords/linear-separability","display_name":"linear separability","score":0.8190000057220459},{"id":"https://openalex.org/keywords/high-dimensional-geometry","display_name":"high-dimensional geometry","score":0.6669999957084656},{"id":"https://openalex.org/keywords/domain-transfer","display_name":"domain transfer","score":0.4690000116825104},{"id":"https://openalex.org/keywords/evaluation-indicators","display_name":"evaluation metrics","score":0.546999990940094}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7404481172561646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7213484644889832},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6982792019844055},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.6796571016311646},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6213854551315308},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5866074562072754},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.585988461971283},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.5713768005371094},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.550591766834259},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.49046751856803894},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4769645929336548},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4120231866836548},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12341061234474182},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2305.16562","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.16562","pdf_url":"https://arxiv.org/pdf/2305.16562","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"doi:10.48550/arxiv.2305.16562","doi":"https://doi.org/10.48550/arxiv.2305.16562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2305.16562","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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:2305.16562","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.16562","pdf_url":"https://arxiv.org/pdf/2305.16562","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":[],"sustainable_development_goals_aurora":[{"display_name":"Quality education","id":"https://metadata.un.org/sdg/4","score":0.6800000071525574}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4378713423.pdf","grobid_xml":"https://content.openalex.org/works/W4378713423.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4220926404","https://openalex.org/W3123344745","https://openalex.org/W3148060700","https://openalex.org/W3080681248","https://openalex.org/W4376646226","https://openalex.org/W3047177827","https://openalex.org/W4287685660","https://openalex.org/W2057778272","https://openalex.org/W4319302697","https://openalex.org/W2163389298"],"abstract_inverted_index":{"Unsupervised":[0],"learning":[1,88],"has":[2],"recently":[3],"significantly":[4],"gained":[5],"in":[6,36,50,80,113,145,168,191],"popularity,":[7],"especially":[8],"with":[9,47],"deep":[10,90],"learning-based":[11],"approaches.":[12],"Despite":[13],"numerous":[14],"successes":[15],"and":[16,32,121,158,165],"approaching":[17],"supervised-level":[18],"performance":[19],"on":[20,142],"a":[21,51,98,114],"variety":[22],"of":[23,43,77,89,133,151,162],"academic":[24],"benchmarks,":[25],"it":[26,54,106],"is":[27,55,83,107,178],"still":[28],"hard":[29],"to":[30,39,65,73,96,108],"train":[31],"evaluate":[33],"SSL":[34],"models":[35],"practice":[37],"due":[38],"the":[40,44,75,111,119,147,160,169],"unsupervised":[41,193],"nature":[42],"problem.":[45],"Even":[46],"networks":[48],"trained":[49],"supervised":[52],"fashion,":[53],"often":[56],"unclear":[57],"whether":[58],"they":[59],"will":[60],"perform":[61],"well":[62],"when":[63],"transferred":[64],"another":[66],"domain.":[67],"Past":[68],"works":[69,94],"are":[70,183],"generally":[71],"limited":[72],"assessing":[74],"amount":[76],"information":[78],"contained":[79],"embeddings,":[81],"which":[82],"most":[84],"relevant":[85],"for":[86,130],"self-supervised":[87,152],"neural":[91],"networks.":[92],"This":[93],"chooses":[95],"follow":[97],"different":[99],"approach:":[100],"can":[101,186],"we":[102],"quantify":[103],"how":[104],"easy":[105],"linearly":[109],"separate":[110],"data":[112],"stable":[115],"way?":[116],"We":[117,135,154],"survey":[118],"literature":[120],"uncover":[122],"three":[123],"methods":[124],"that":[125,175,185],"could":[126],"be":[127],"potentially":[128],"used":[129],"evaluating":[131],"quality":[132,190],"representations.":[134],"also":[136],"introduce":[137],"one":[138],"novel":[139],"method":[140],"based":[141],"recent":[143],"advances":[144],"understanding":[146],"high-dimensional":[148],"geometric":[149],"structure":[150],"learning.":[153],"conduct":[155],"extensive":[156],"experiments":[157],"study":[159],"properties":[161],"these":[163],"metrics":[164,184],"ones":[166],"introduced":[167],"previous":[170],"work.":[171],"Our":[172],"results":[173],"suggest":[174],"while":[176],"there":[177,182],"no":[179],"free":[180],"lunch,":[181],"robustly":[187],"estimate":[188],"embedding":[189],"an":[192],"way.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-10-10T00:00:00"}
