{"id":"https://openalex.org/W4281710985","doi":"https://doi.org/10.48550/arxiv.2206.00048","title":"PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs","display_name":"PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs","publication_year":2022,"publication_date":"2022-05-31","ids":{"openalex":"https://openalex.org/W4281710985","doi":"https://doi.org/10.48550/arxiv.2206.00048"},"language":"en","primary_location":{"id":"pmh:oai:openaccess.city.ac.uk:31342","is_oa":false,"landing_page_url":"https://openaccess.city.ac.uk/view/creators_id/christos=2Etzelepis.html>","pdf_url":null,"source":{"id":"https://openalex.org/S4306401940","display_name":"City Research Online (City University London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I180825142","host_organization_name":"City, University of London","host_organization_lineage":["https://openalex.org/I180825142"],"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":"PeerReviewed"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2206.00048","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5105300467","display_name":"J. E. Oldfield","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Oldfield, James","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089816358","display_name":"Christos Tzelepis","orcid":"https://orcid.org/0000-0002-2036-9089"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tzelepis, Christos","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050734738","display_name":"Yannis Panagakis","orcid":"https://orcid.org/0000-0003-0153-5210"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Panagakis, Yannis","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017437981","display_name":"Mihalis A. Nicolaou","orcid":"https://orcid.org/0000-0001-9175-477X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nicolaou, Mihalis A.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5031205865","display_name":"Ioannis Patras","orcid":"https://orcid.org/0000-0003-3913-4738"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Patras, Ioannis","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5105300467"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":10,"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":0.995199978351593,"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":0.995199978351593,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.9761000275611877,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9757999777793884,"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":0.8321369886398315},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5964666604995728},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5860741138458252},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5664900541305542},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5638716816902161},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5543338060379028},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5505124926567078},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.47993189096450806},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4355704188346863},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.42917585372924805},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42454826831817627},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4038705825805664},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.1000741720199585}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8321369886398315},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5964666604995728},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5860741138458252},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5664900541305542},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5638716816902161},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5543338060379028},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5505124926567078},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.47993189096450806},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4355704188346863},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.42917585372924805},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42454826831817627},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4038705825805664},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.1000741720199585},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"pmh:oai:openaccess.city.ac.uk:31342","is_oa":false,"landing_page_url":"https://openaccess.city.ac.uk/view/creators_id/christos=2Etzelepis.html>","pdf_url":null,"source":{"id":"https://openalex.org/S4306401940","display_name":"City Research Online (City University London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I180825142","host_organization_name":"City, University of London","host_organization_lineage":["https://openalex.org/I180825142"],"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":"PeerReviewed"},{"id":"pmh:oai:arXiv.org:2206.00048","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.00048","pdf_url":"https://arxiv.org/pdf/2206.00048","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":"","raw_type":"text"},{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/84510","is_oa":false,"landing_page_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/84510","pdf_url":null,"source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"},{"id":"pmh:oai:qmro.qmul.ac.uk:123456789/91798","is_oa":false,"landing_page_url":"https://qmro.qmul.ac.uk/xmlui/handle/123456789/91798","pdf_url":null,"source":{"id":"https://openalex.org/S4306400530","display_name":"Queen Mary Research Online (Queen Mary University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"doi:10.48550/arxiv.2206.00048","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2206.00048","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2206.00048","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.00048","pdf_url":"https://arxiv.org/pdf/2206.00048","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":"","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5299999713897705}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W1647606319","https://openalex.org/W4390494008","https://openalex.org/W2053596378","https://openalex.org/W2922442631","https://openalex.org/W2168523118","https://openalex.org/W2073639911","https://openalex.org/W3112772842"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,15,29,92,112,156,170],"the":[3,23,30,108,127,159,162],"understanding":[4],"of":[5,33,65,138,149,161,172],"Generative":[6],"Adversarial":[7],"Networks":[8],"(GANs)":[9],"have":[10],"led":[11],"to":[12,42,49,132,158],"remarkable":[13],"progress":[14],"visual":[16],"editing":[17,118],"and":[18,46,89,152],"synthesis":[19],"tasks,":[20],"capitalizing":[21],"on":[22,107,145],"rich":[24],"semantics":[25],"that":[26,55,82,126,135],"are":[27,39,47,99],"embedded":[28],"latent":[31],"spaces":[32],"pre-trained":[34],"GANs.":[35],"However,":[36],"existing":[37],"methods":[38],"often":[40],"tailored":[41],"specific":[43],"GAN":[44,150],"architectures":[45,151],"limited":[48],"either":[50],"discovering":[51],"global":[52],"semantic":[53],"directions":[54],"do":[56],"not":[57],"facilitate":[58],"localized":[59,182],"control,":[60],"or":[61,71],"require":[62],"some":[63],"form":[64],"supervision":[66],"through":[67],"manually":[68],"provided":[69],"regions":[70],"segmentation":[72],"masks.":[73],"In":[74,122],"this":[75],"light,":[76],"we":[77,124],"present":[78],"an":[79,93],"architecture-agnostic":[80],"approach":[81],"jointly":[83],"discovers":[84],"factors":[85,98,130],"representing":[86],"spatial":[87],"parts":[88],"their":[90],"appearances":[91],"entirely":[94],"unsupervised":[95],"fashion.":[96],"These":[97],"obtained":[100],"by":[101],"applying":[102],"a":[103,146],"semi-nonnegative":[104],"tensor":[105],"factorization":[106],"feature":[109],"maps,":[110],"which":[111],"turn":[113],"enables":[114],"context-aware":[115],"local":[116],"image":[117],"with":[119],"pixel-level":[120],"control.":[121,183],"addition,":[123],"show":[125,154],"discovered":[128],"appearance":[129],"correspond":[131],"saliency":[133],"maps":[134],"localize":[136],"concepts":[137],"interest,":[139],"without":[140],"using":[141],"any":[142],"labels.":[143],"Experiments":[144],"wide":[147],"range":[148],"datasets":[153],"that,":[155],"comparison":[157],"state":[160],"art,":[163],"our":[164],"method":[165],"is":[166,186],"far":[167],"more":[168,180],"efficient":[169],"terms":[171],"training":[173],"time":[174],"and,":[175],"most":[176],"importantly,":[177],"provides":[178],"much":[179],"accurate":[181],"Our":[184],"code":[185],"available":[187],"at:":[188],"https://github.com/james-oldfield/PandA.":[189]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}