{"id":"https://openalex.org/W2996510349","doi":"https://doi.org/10.1109/icpr48806.2021.9412523","title":"Phase Retrieval Using Conditional Generative Adversarial Networks","display_name":"Phase Retrieval Using Conditional Generative Adversarial Networks","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W2996510349","doi":"https://doi.org/10.1109/icpr48806.2021.9412523","mag":"2996510349"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412523","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412523","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1912.04981","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046896904","display_name":"Tobias Uelwer","orcid":null},"institutions":[{"id":"https://openalex.org/I44260953","display_name":"Heinrich Heine University D\u00fcsseldorf","ror":"https://ror.org/024z2rq82","country_code":"DE","type":"education","lineage":["https://openalex.org/I44260953"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tobias Uelwer","raw_affiliation_strings":["Heinrich-Heine-Universtit\u00e4t D\u00fcsseldorf, D\u00fcsseldorf, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Heinrich-Heine-Universtit\u00e4t D\u00fcsseldorf, D\u00fcsseldorf, Germany","institution_ids":["https://openalex.org/I44260953"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014432034","display_name":"Alexander Oberstra\u00df","orcid":null},"institutions":[{"id":"https://openalex.org/I44260953","display_name":"Heinrich Heine University D\u00fcsseldorf","ror":"https://ror.org/024z2rq82","country_code":"DE","type":"education","lineage":["https://openalex.org/I44260953"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alexander Oberstra\u00df","raw_affiliation_strings":["Heinrich-Heine-Universtit\u00e4t D\u00fcsseldorf, D\u00fcsseldorf, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Heinrich-Heine-Universtit\u00e4t D\u00fcsseldorf, D\u00fcsseldorf, Germany","institution_ids":["https://openalex.org/I44260953"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062835183","display_name":"Stefan Harmeling","orcid":null},"institutions":[{"id":"https://openalex.org/I44260953","display_name":"Heinrich Heine University D\u00fcsseldorf","ror":"https://ror.org/024z2rq82","country_code":"DE","type":"education","lineage":["https://openalex.org/I44260953"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Stefan Harmeling","raw_affiliation_strings":["Heinrich-Heine-Universtit\u00e4t D\u00fcsseldorf, D\u00fcsseldorf, Germany","University of D\u00fcsseldorf"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Heinrich-Heine-Universtit\u00e4t D\u00fcsseldorf, D\u00fcsseldorf, Germany","institution_ids":["https://openalex.org/I44260953"]},{"raw_affiliation_string":"University of D\u00fcsseldorf","institution_ids":["https://openalex.org/I44260953"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I44260953"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"731","last_page":"738"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11183","display_name":"Advanced X-ray Imaging Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11183","display_name":"Advanced X-ray Imaging Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10638","display_name":"Optical measurement and interference techniques","score":0.9822999835014343,"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/T11338","display_name":"Advancements in Photolithography Techniques","score":0.9778000116348267,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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":0.806114673614502},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.7236486673355103},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5673837661743164},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5658538341522217},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5545852184295654},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5285854339599609},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5250639319419861},{"id":"https://openalex.org/keywords/phase-retrieval","display_name":"Phase retrieval","score":0.46967822313308716},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4693637192249298},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4457797408103943},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3309972584247589},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.3289242386817932},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11060971021652222}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.806114673614502},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.7236486673355103},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5673837661743164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5658538341522217},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5545852184295654},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5285854339599609},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5250639319419861},{"id":"https://openalex.org/C81793267","wikidata":"https://www.wikidata.org/wiki/Q7180962","display_name":"Phase retrieval","level":3,"score":0.46967822313308716},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4693637192249298},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4457797408103943},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3309972584247589},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.3289242386817932},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11060971021652222},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412523","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412523","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1912.04981","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.04981","pdf_url":"https://arxiv.org/pdf/1912.04981","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":"doi:10.48550/arxiv.1912.04981","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1912.04981","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":"Preprint"},{"id":"mag:2996510349","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1912.04981","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.04981","pdf_url":"https://arxiv.org/pdf/1912.04981","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":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2996510349.pdf","grobid_xml":"https://content.openalex.org/works/W2996510349.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W1484412996","https://openalex.org/W1522301498","https://openalex.org/W1834627138","https://openalex.org/W1963623641","https://openalex.org/W1973207880","https://openalex.org/W1994007038","https://openalex.org/W2002182974","https://openalex.org/W2007593159","https://openalex.org/W2044613770","https://openalex.org/W2047602908","https://openalex.org/W2099471712","https://openalex.org/W2101769813","https://openalex.org/W2112796928","https://openalex.org/W2125389028","https://openalex.org/W2126320627","https://openalex.org/W2133665775","https://openalex.org/W2142949395","https://openalex.org/W2173520492","https://openalex.org/W2233912973","https://openalex.org/W2256685641","https://openalex.org/W2593414223","https://openalex.org/W2750384547","https://openalex.org/W2802180609","https://openalex.org/W2811431687","https://openalex.org/W2821662226","https://openalex.org/W2888234127","https://openalex.org/W2949117887","https://openalex.org/W2951004968","https://openalex.org/W2953651551","https://openalex.org/W2962999772","https://openalex.org/W2963073572","https://openalex.org/W2963073614","https://openalex.org/W2963125871","https://openalex.org/W2963420272","https://openalex.org/W2963491312","https://openalex.org/W2999145631","https://openalex.org/W3098248527","https://openalex.org/W3102206315","https://openalex.org/W3103875018","https://openalex.org/W6628955290","https://openalex.org/W6678946985","https://openalex.org/W6704369950","https://openalex.org/W6729966448"],"related_works":["https://openalex.org/W2785532149","https://openalex.org/W2765106683","https://openalex.org/W2912289011","https://openalex.org/W2624918875","https://openalex.org/W3162265811","https://openalex.org/W2914848199","https://openalex.org/W2999091639","https://openalex.org/W2953141406","https://openalex.org/W3134232992","https://openalex.org/W2269778407","https://openalex.org/W3034234422","https://openalex.org/W2921743338","https://openalex.org/W3168420341","https://openalex.org/W2963901583","https://openalex.org/W3133520805","https://openalex.org/W2752497613","https://openalex.org/W3155102340","https://openalex.org/W3046618572","https://openalex.org/W2297651918","https://openalex.org/W1831449718"],"abstract_inverted_index":{"In":[0,49],"this":[1],"paper,":[2],"we":[3],"propose":[4],"the":[5,24,52,56,76,79],"application":[6],"of":[7,23],"conditional":[8],"generative":[9,47],"adversarial":[10],"networks":[11],"to":[12,31,41,60,75,112],"solve":[13],"various":[14],"phase":[15,81],"retrieval":[16,82],"problems.":[17],"We":[18,66],"show":[19],"that":[20,37,69,85,97],"including":[21],"knowledge":[22],"measurement":[25],"process":[26],"at":[27,34],"training":[28],"time":[29,36],"leads":[30],"an":[32],"optimization":[33],"test":[35],"is":[38,110],"more":[39,63],"robust":[40,111],"initialization":[42],"than":[43],"existing":[44,95],"approaches":[45],"involving":[46],"models.":[48],"addition,":[50],"conditioning":[51],"generator":[53],"network":[54],"on":[55,100],"measurements":[57],"enables":[58],"us":[59],"achieve":[61],"much":[62],"detailed":[64],"results.":[65],"empirically":[67],"demonstrate":[68],"these":[70],"advantages":[71],"provide":[72],"meaningful":[73],"solutions":[74],"Fourier":[77],"and":[78,84,114],"compressive":[80],"problem":[83],"our":[86,108],"method":[87],"outperforms":[88],"well-established":[89],"projection-based":[90],"methods":[91,96],"as":[92,94],"well":[93],"are":[98],"based":[99],"neural":[101],"networks.":[102],"Like":[103],"other":[104],"deep":[105],"learning":[106],"methods,":[107],"approach":[109],"noise":[113],"can":[115],"therefore":[116],"be":[117],"useful":[118],"for":[119],"real-world":[120],"applications.":[121]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
