{"id":"https://openalex.org/W4330336543","doi":"https://doi.org/10.48550/arxiv.2303.10191","title":"Unsupervised Domain Transfer with Conditional Invertible Neural Networks","display_name":"Unsupervised Domain Transfer with Conditional Invertible Neural Networks","publication_year":2023,"publication_date":"2023-03-17","ids":{"openalex":"https://openalex.org/W4330336543","doi":"https://doi.org/10.48550/arxiv.2303.10191"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2303.10191","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.10191","pdf_url":"https://arxiv.org/pdf/2303.10191","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/2303.10191","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014296305","display_name":"Kris K. Dreher","orcid":"https://orcid.org/0000-0002-9179-9414"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Dreher, Kris K.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022783609","display_name":"Leonardo Ayala","orcid":"https://orcid.org/0000-0002-3574-2085"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ayala, Leonardo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103025262","display_name":"Melanie Schellenberg","orcid":"https://orcid.org/0000-0002-7911-5622"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schellenberg, Melanie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031227200","display_name":"Marco H\u00fcbner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"H\u00fcbner, Marco","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028754172","display_name":"Jan-Hinrich N\u00f6lke","orcid":"https://orcid.org/0000-0001-7600-3839"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"N\u00f6lke, Jan-Hinrich","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080597416","display_name":"Tim Adler","orcid":"https://orcid.org/0000-0002-3424-6629"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adler, Tim J.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028657555","display_name":"Silvia Seidlitz","orcid":"https://orcid.org/0000-0002-1122-4793"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seidlitz, Silvia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064056688","display_name":"Jan Sellner","orcid":"https://orcid.org/0000-0003-4469-8343"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sellner, Jan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047810855","display_name":"Alexander Studier\u2010Fischer","orcid":"https://orcid.org/0000-0001-8682-9300"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Studier-Fischer, Alexander","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079169749","display_name":"Janek Gr\u00f6hl","orcid":"https://orcid.org/0000-0002-5332-4856"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gr\u00f6hl, Janek","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068403686","display_name":"Felix Nickel","orcid":"https://orcid.org/0000-0001-6066-8238"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nickel, Felix","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110454166","display_name":"Ullrich K\u00f6the","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"K\u00f6the, Ullrich","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080028621","display_name":"Alexander Seitel","orcid":"https://orcid.org/0000-0002-5919-9646"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seitel, Alexander","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5023493127","display_name":"Lena Maier\u2010Hein","orcid":"https://orcid.org/0000-0003-4910-9368"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maier-Hein, Lena","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":14,"corresponding_author_ids":["https://openalex.org/A5014296305"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11856","display_name":"Thermography and Photoacoustic Techniques","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9211999773979187,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7595963478088379},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6779459714889526},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5276787877082825},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5113958716392517},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5080447196960449},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.484809547662735},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4775325059890747},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4703540802001953},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4401566982269287},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42171648144721985},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.41693738102912903},{"id":"https://openalex.org/keywords/invertible-matrix","display_name":"Invertible matrix","score":0.41531726717948914},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.41035839915275574},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13308170437812805}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7595963478088379},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6779459714889526},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5276787877082825},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5113958716392517},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5080447196960449},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.484809547662735},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4775325059890747},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4703540802001953},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4401566982269287},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42171648144721985},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.41693738102912903},{"id":"https://openalex.org/C96442724","wikidata":"https://www.wikidata.org/wiki/Q242188","display_name":"Invertible matrix","level":2,"score":0.41531726717948914},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.41035839915275574},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13308170437812805},{"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2303.10191","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.10191","pdf_url":"https://arxiv.org/pdf/2303.10191","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.2303.10191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2303.10191","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-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2303.10191","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.10191","pdf_url":"https://arxiv.org/pdf/2303.10191","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":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4330336543.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4206357785","https://openalex.org/W4281381188","https://openalex.org/W3192840557","https://openalex.org/W2951211570","https://openalex.org/W4375928479","https://openalex.org/W3167935049","https://openalex.org/W3023427754","https://openalex.org/W3131673289","https://openalex.org/W4393011546","https://openalex.org/W3198847674"],"abstract_inverted_index":{"Synthetic":[0],"medical":[1],"image":[2],"generation":[3,148,182],"has":[4,43],"evolved":[5],"as":[6,174],"a":[7,79,91],"key":[8],"technique":[9],"for":[10,178],"neural":[11,87],"network":[12,105],"training":[13,63,106],"and":[14,27,40,104,136,153,166,189],"validation.":[15],"A":[16],"core":[17],"challenge,":[18],"however,":[19],"remains":[20],"in":[21,48,55,183],"the":[22,49,147,155,158,184],"domain":[23,33,80,169],"gap":[24],"between":[25],"simulations":[26],"real":[28],"data.":[29],"While":[30],"deep":[31],"learning-based":[32],"transfer":[34,81,170],"using":[35],"Cycle":[36],"Generative":[37],"Adversarial":[38],"Networks":[39],"similar":[41],"architectures":[42],"led":[44],"to":[45,61,124,141],"substantial":[46],"progress":[47],"field,":[50],"there":[51],"are":[52],"use":[53],"cases":[54],"which":[56],"state-of-the-art":[57],"approaches":[58],"still":[59],"fail":[60],"generate":[62],"images":[64],"that":[65],"produce":[66],"convincing":[67],"results":[68],"on":[69,84,160],"relevant":[70],"downstream":[71,162],"tasks.":[72],"Here,":[73],"we":[74,121],"address":[75],"this":[76],"issue":[77],"with":[78,111],"approach":[82],"based":[83],"conditional":[85],"invertible":[86,102],"networks":[88],"(cINNs).":[89],"As":[90],"particular":[92],"advantage,":[93],"our":[94,117,144],"method":[95,145,177],"inherently":[96],"guarantees":[97],"cycle":[98],"consistency":[99],"through":[100],"its":[101],"architecture,":[103],"can":[107],"efficiently":[108],"be":[109],"conducted":[110],"maximum":[112],"likelihood":[113],"training.":[114],"To":[115],"showcase":[116],"method's":[118],"generic":[119],"applicability,":[120],"apply":[122],"it":[123],"two":[125,161],"spectral":[126,151,187],"imaging":[127,134,188],"modalities":[128],"at":[129],"different":[130],"scales,":[131],"namely":[132],"hyperspectral":[133],"(pixel-level)":[135],"photoacoustic":[137],"tomography":[138],"(image-level).":[139],"According":[140],"comprehensive":[142],"experiments,":[143],"enables":[146],"of":[149,157,186],"realistic":[150,179],"data":[152,181],"outperforms":[154],"state":[156],"art":[159],"classification":[163],"tasks":[164],"(binary":[165],"multi-class).":[167],"cINN-based":[168],"could":[171],"thus":[172],"evolve":[173],"an":[175],"important":[176],"synthetic":[180],"field":[185],"beyond.":[190]},"counts_by_year":[],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
