{"id":"https://openalex.org/W3034418897","doi":"https://doi.org/10.24963/ijcai.2020/408","title":"Unsupervised Representation Learning by Predicting Random Distances","display_name":"Unsupervised Representation Learning by Predicting Random Distances","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3034418897","doi":"https://doi.org/10.24963/ijcai.2020/408","mag":"3034418897"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2020/408","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/408","pdf_url":"https://www.ijcai.org/proceedings/2020/0408.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2020/0408.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100401021","display_name":"Wang Hu","orcid":"https://orcid.org/0000-0002-5319-7463"},"institutions":[{"id":"https://openalex.org/I5681781","display_name":"University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I5681781"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Hu Wang","raw_affiliation_strings":["The University of Adelaide","The University of Adelaide, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Adelaide","institution_ids":["https://openalex.org/I5681781"]},{"raw_affiliation_string":"The University of Adelaide, Australia","institution_ids":["https://openalex.org/I5681781"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039104219","display_name":"Guansong Pang","orcid":"https://orcid.org/0000-0002-9877-2716"},"institutions":[{"id":"https://openalex.org/I5681781","display_name":"University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I5681781"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Guansong Pang","raw_affiliation_strings":["The University of Adelaide","The University of Adelaide, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Adelaide","institution_ids":["https://openalex.org/I5681781"]},{"raw_affiliation_string":"The University of Adelaide, Australia","institution_ids":["https://openalex.org/I5681781"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006294869","display_name":"Chunhua Shen","orcid":"https://orcid.org/0000-0002-8648-8718"},"institutions":[{"id":"https://openalex.org/I5681781","display_name":"University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I5681781"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Chunhua Shen","raw_affiliation_strings":["The University of Adelaide","The University of Adelaide, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Adelaide","institution_ids":["https://openalex.org/I5681781"]},{"raw_affiliation_string":"The University of Adelaide, Australia","institution_ids":["https://openalex.org/I5681781"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101646727","display_name":"Congbo Ma","orcid":"https://orcid.org/0000-0002-3270-5609"},"institutions":[{"id":"https://openalex.org/I5681781","display_name":"University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I5681781"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Congbo Ma","raw_affiliation_strings":["The University of Adelaide","The University of Adelaide, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Adelaide","institution_ids":["https://openalex.org/I5681781"]},{"raw_affiliation_string":"The University of Adelaide, Australia","institution_ids":["https://openalex.org/I5681781"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100401021"],"corresponding_institution_ids":["https://openalex.org/I5681781"],"apc_list":null,"apc_paid":null,"fwci":5.6827,"has_fulltext":false,"cited_by_count":65,"citation_normalized_percentile":{"value":0.96702723,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2950","last_page":"2956"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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":0.9988999962806702,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9975000023841858,"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/computer-science","display_name":"Computer science","score":0.7762688398361206},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7208116054534912},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7172666788101196},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.6236376762390137},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6211965084075928},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5779585838317871},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5494788885116577},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5295741558074951},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5164797306060791},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.51283198595047},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4879316985607147},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43754395842552185},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.43542858958244324},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.4164176881313324}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7762688398361206},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7208116054534912},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7172666788101196},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.6236376762390137},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6211965084075928},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5779585838317871},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5494788885116577},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5295741558074951},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5164797306060791},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.51283198595047},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4879316985607147},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43754395842552185},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.43542858958244324},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.4164176881313324},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2020/408","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/408","pdf_url":"https://www.ijcai.org/proceedings/2020/0408.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2020/408","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/408","pdf_url":"https://www.ijcai.org/proceedings/2020/0408.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3034418897.pdf","grobid_xml":"https://content.openalex.org/works/W3034418897.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W343636949","https://openalex.org/W1519844777","https://openalex.org/W1565746575","https://openalex.org/W1614298861","https://openalex.org/W1672197616","https://openalex.org/W1977556410","https://openalex.org/W2041836310","https://openalex.org/W2100495367","https://openalex.org/W2144902422","https://openalex.org/W2156838815","https://openalex.org/W2157444450","https://openalex.org/W2487442924","https://openalex.org/W2609883120","https://openalex.org/W2740387900","https://openalex.org/W2785325870","https://openalex.org/W2786088545","https://openalex.org/W2807955733","https://openalex.org/W2963226019","https://openalex.org/W2963420272","https://openalex.org/W2963684088","https://openalex.org/W2964037671","https://openalex.org/W2964067469","https://openalex.org/W2979473749","https://openalex.org/W2997574889","https://openalex.org/W4297808394","https://openalex.org/W4313169793"],"related_works":["https://openalex.org/W4285233543","https://openalex.org/W3174759195","https://openalex.org/W4230838436","https://openalex.org/W3167013339","https://openalex.org/W4287121366","https://openalex.org/W60493759","https://openalex.org/W4308619659","https://openalex.org/W3213069564","https://openalex.org/W2750141660","https://openalex.org/W4378421684"],"abstract_inverted_index":{"Deep":[0],"neural":[1,90],"networks":[2,91],"have":[3],"gained":[4],"great":[5],"success":[6],"in":[7,43,75,96,132],"a":[8,97,104,150],"broad":[9],"range":[10],"of":[11],"tasks":[12],"due":[13],"to":[14,18,33,57,80,92,108,123],"its":[15],"remarkable":[16],"capability":[17],"learn":[19,35,81,124],"semantically":[20],"rich":[21],"features":[22,82],"from":[23],"high-dimensional":[24],"data.":[25],"However,":[26],"they":[27],"often":[28],"require":[29],"large-scale":[30],"labelled":[31,63,86],"data":[32,64,87,94],"successfully":[34],"such":[36,47],"features,":[37],"which":[38],"significantly":[39],"hinders":[40],"their":[41,55],"adaption":[42],"unsupervised":[44,70],"learning":[45,71],"tasks,":[46],"as":[48],"anomaly":[49,156],"detection":[50,157],"and":[51,53,158],"clustering,":[52],"limits":[54],"applications":[56],"critical":[58],"domains":[59],"where":[60],"obtaining":[61],"massive":[62],"is":[65,103,121,162],"prohibitively":[66],"expensive.":[67],"To":[68,113],"enable":[69],"on":[72,139],"those":[73],"domains,":[74],"this":[76],"work":[77],"we":[78],"propose":[79],"without":[83],"using":[84],"any":[85],"by":[88],"training":[89],"predict":[93,115],"distances":[95],"randomly":[98,134],"projected":[99,135],"space.":[100,136],"Random":[101],"mapping":[102],"theoretically":[105],"proven":[106],"approach":[107],"obtain":[109],"approximately":[110],"preserved":[111],"distances.":[112],"well":[114],"these":[116],"distances,":[117],"the":[118,133],"representation":[119],"learner":[120],"optimised":[122],"genuine":[125],"class":[126],"structures":[127],"that":[128,144],"are":[129],"implicitly":[130],"embedded":[131],"Empirical":[137],"results":[138],"19":[140],"real-world":[141],"datasets":[142],"show":[143],"our":[145],"learned":[146],"representations":[147],"substantially":[148],"outperform":[149],"few":[151],"state-of-the-art":[152],"methods":[153],"for":[154],"both":[155],"clustering":[159],"tasks.":[160],"Code":[161],"available":[163],"at:":[164],"\\url{https://git.io/RDP}":[165]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":17},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}