{"id":"https://openalex.org/W4385219041","doi":"https://doi.org/10.48550/arxiv.2307.11375","title":"LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space","display_name":"LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space","publication_year":2023,"publication_date":"2023-07-21","ids":{"openalex":"https://openalex.org/W4385219041","doi":"https://doi.org/10.48550/arxiv.2307.11375"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2307.11375","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.11375","pdf_url":"https://arxiv.org/pdf/2307.11375","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2307.11375","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067471614","display_name":"Lorenzo Tronchin","orcid":"https://orcid.org/0000-0001-6512-9444"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tronchin, Lorenzo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063734949","display_name":"Minh H. Vu","orcid":"https://orcid.org/0000-0002-2391-1419"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vu, Minh H.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003216983","display_name":"Paolo Soda","orcid":"https://orcid.org/0000-0003-2621-072X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Soda, Paolo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5035857033","display_name":"Tommy L\u00f6fstedt","orcid":"https://orcid.org/0000-0001-7119-7646"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"L\u00f6fstedt, Tommy","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5067471614"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"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":0.9983999729156494,"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":0.9983999729156494,"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/T10862","display_name":"AI in cancer detection","score":0.9976999759674072,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9940000176429749,"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/overfitting","display_name":"Overfitting","score":0.9248672723770142},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7568963170051575},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.711236834526062},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5547698736190796},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.549964964389801},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5372142195701599},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5191942453384399},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4840244948863983},{"id":"https://openalex.org/keywords/diversity","display_name":"Diversity (politics)","score":0.4644800126552582},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.42279714345932007},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.41603603959083557},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4106955826282501},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40034589171409607},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37155675888061523},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14231684803962708},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.12853866815567017}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9248672723770142},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7568963170051575},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.711236834526062},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5547698736190796},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.549964964389801},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5372142195701599},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5191942453384399},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4840244948863983},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.4644800126552582},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.42279714345932007},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41603603959083557},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4106955826282501},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40034589171409607},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37155675888061523},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14231684803962708},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.12853866815567017},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2307.11375","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.11375","pdf_url":"https://arxiv.org/pdf/2307.11375","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2307.11375","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2307.11375","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2307.11375","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.11375","pdf_url":"https://arxiv.org/pdf/2307.11375","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385219041.pdf","grobid_xml":"https://content.openalex.org/works/W4385219041.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4384517922","https://openalex.org/W4377088509","https://openalex.org/W4322617773"],"abstract_inverted_index":{"Data":[0],"Augmentation":[1],"(DA)":[2],"is":[3,137,190],"a":[4,44,97,159],"technique":[5],"to":[6,60,128,140],"increase":[7],"the":[8,13,51,102,130,141,144,156],"quantity":[9],"and":[10,16,21,67,71,74,121,134,143,187],"diversity":[11,70,104,133],"of":[12,53,105,150,158],"training":[14],"data,":[15],"by":[17,46],"that":[18,100,153],"alleviate":[19],"overfitting":[20],"improve":[22],"generalisation.":[23],"However,":[24,56],"standard":[25,167],"DA":[26,92,98,112,168],"produces":[27],"synthetic":[28,48,131,181],"data":[29],"for":[30,109],"augmentation":[31],"with":[32,177],"limited":[33],"diversity.":[34,188],"Generative":[35],"Adversarial":[36],"Networks":[37],"(GANs)":[38],"may":[39],"unlock":[40],"additional":[41],"information":[42],"in":[43,91,111,175],"dataset":[45,142],"generating":[47],"samples":[49,81,182],"having":[50],"appearance":[52],"real":[54],"images.":[55],"these":[57],"models":[58],"struggle":[59],"simultaneously":[61],"address":[62],"three":[63],"key":[64],"requirements:":[65],"fidelity":[66],"high-quality":[68,80],"samples;":[69],"mode":[72,86,185],"coverage;":[73],"fast":[75],"sampling.":[76,172],"Indeed,":[77],"GANs":[78],"generate":[79],"rapidly,":[82],"but":[83],"have":[84],"poor":[85],"coverage,":[87],"limiting":[88],"their":[89],"adoption":[90],"applications.":[93,113],"We":[94],"propose":[95],"LatentAugment,":[96],"strategy":[99],"overcomes":[101],"low":[103],"GANs,":[106],"opening":[107],"up":[108],"use":[110],"Without":[114],"external":[115],"supervision,":[116],"LatentAugment":[117,154,180],"modifies":[118],"latent":[119,125],"vectors":[120],"moves":[122],"them":[123],"into":[124],"space":[126],"regions":[127],"maximise":[129],"images'":[132],"fidelity.":[135],"It":[136],"also":[138],"agnostic":[139],"downstream":[145],"task.":[146],"A":[147],"wide":[148],"set":[149],"experiments":[151],"shows":[152],"improves":[155],"generalisation":[157],"deep":[160],"model":[161],"translating":[162],"from":[163],"MRI-to-CT":[164],"beating":[165],"both":[166],"as":[169],"well":[170],"GAN-based":[171,178],"Moreover,":[173],"still":[174],"comparison":[176],"sampling,":[179],"show":[183],"superior":[184],"coverage":[186],"Code":[189],"available":[191],"at:":[192],"https://github.com/ltronchin/LatentAugment.":[193]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2023-07-25T00:00:00"}