A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Generative adversarial networks (GANs) have recently become a hot research topic; however, they have been studied since 2014, and a large number of algorithms have been proposed. Nevertheless, few comprehensive studies explain the connections among different GAN variants and how they have evolved. I...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on knowledge and data engineering 2023-04, Vol.35 (4), p.3313-3332 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3332 |
|---|---|
| container_issue | 4 |
| container_start_page | 3313 |
| container_title | IEEE transactions on knowledge and data engineering |
| container_volume | 35 |
| creator | Gui, Jie Sun, Zhenan Wen, Yonggang Tao, Dacheng Ye, Jieping |
| description | Generative adversarial networks (GANs) have recently become a hot research topic; however, they have been studied since 2014, and a large number of algorithms have been proposed. Nevertheless, few comprehensive studies explain the connections among different GAN variants and how they have evolved. In this paper, we attempt to provide a review of the various GAN methods from the perspectives of algorithms, theory, and applications. First, the motivations, mathematical representations, and structures of most GAN algorithms are introduced in detail, and we compare their commonalities and differences. Second, theoretical issues related to GANs are investigated. Finally, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, the medical field, and data science are discussed. |
| doi_str_mv | 10.1109/TKDE.2021.3130191 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_9625798</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9625798</ieee_id><sourcerecordid>2784550391</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2924-7437d73656bfc3b790b4c63764f35eceeea428582dc62a551574c0ef168486f43</originalsourceid><addsrcrecordid>eNo9kE1PwkAQhjdGExH9AcbLJl4t7vduvTWIaCQaDZ6bZTuVInTrboHw7y2BeJr38D4zkweha0oGlJL0fvr6OBowwuiAU05oSk9Qj0ppEtbl0y4TQRPBhT5HFzEuCCFGG9pDHxn-hE0FW-xrPIYagm2rDeCs2ECINlR2id-g3frwEx9wtvz2oWrnq3iHp3PwYXeHbV3grGmWletIX8dLdFbaZYSr4-yjr6fRdPicTN7HL8NskjiWMpFowXWhuZJqVjo-0ymZCae4VqLkEhwAWMGMNKxwilkpqdTCESipMsKoUvA-uj3sbYL_XUNs84Vfh7o7mTNthJSEp7Rr0UPLBR9jgDJvQrWyYZdTku_N5Xtz-d5cfjTXMTcHpuq--O-nikmdGv4HfJZofQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2784550391</pqid></control><display><type>article</type><title>A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications</title><source>IEEE Electronic Library (IEL)</source><creator>Gui, Jie ; Sun, Zhenan ; Wen, Yonggang ; Tao, Dacheng ; Ye, Jieping</creator><creatorcontrib>Gui, Jie ; Sun, Zhenan ; Wen, Yonggang ; Tao, Dacheng ; Ye, Jieping</creatorcontrib><description>Generative adversarial networks (GANs) have recently become a hot research topic; however, they have been studied since 2014, and a large number of algorithms have been proposed. Nevertheless, few comprehensive studies explain the connections among different GAN variants and how they have evolved. In this paper, we attempt to provide a review of the various GAN methods from the perspectives of algorithms, theory, and applications. First, the motivations, mathematical representations, and structures of most GAN algorithms are introduced in detail, and we compare their commonalities and differences. Second, theoretical issues related to GANs are investigated. Finally, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, the medical field, and data science are discussed.</description><identifier>ISSN: 1041-4347</identifier><identifier>EISSN: 1558-2191</identifier><identifier>DOI: 10.1109/TKDE.2021.3130191</identifier><identifier>CODEN: ITKEEH</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>algorithm ; Algorithms ; applications ; Audio data ; Computer vision ; Data models ; Deep learning ; Generative adversarial networks ; Generators ; Image processing ; Inference algorithms ; Linear programming ; Machine learning algorithms ; Natural language processing ; theory</subject><ispartof>IEEE transactions on knowledge and data engineering, 2023-04, Vol.35 (4), p.3313-3332</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2924-7437d73656bfc3b790b4c63764f35eceeea428582dc62a551574c0ef168486f43</citedby><cites>FETCH-LOGICAL-c2924-7437d73656bfc3b790b4c63764f35eceeea428582dc62a551574c0ef168486f43</cites><orcidid>0000-0003-4029-9935 ; 0000-0001-7225-5449 ; 0000-0002-9450-1759 ; 0000-0002-2751-5114</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9625798$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9625798$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Gui, Jie</creatorcontrib><creatorcontrib>Sun, Zhenan</creatorcontrib><creatorcontrib>Wen, Yonggang</creatorcontrib><creatorcontrib>Tao, Dacheng</creatorcontrib><creatorcontrib>Ye, Jieping</creatorcontrib><title>A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications</title><title>IEEE transactions on knowledge and data engineering</title><addtitle>TKDE</addtitle><description>Generative adversarial networks (GANs) have recently become a hot research topic; however, they have been studied since 2014, and a large number of algorithms have been proposed. Nevertheless, few comprehensive studies explain the connections among different GAN variants and how they have evolved. In this paper, we attempt to provide a review of the various GAN methods from the perspectives of algorithms, theory, and applications. First, the motivations, mathematical representations, and structures of most GAN algorithms are introduced in detail, and we compare their commonalities and differences. Second, theoretical issues related to GANs are investigated. Finally, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, the medical field, and data science are discussed.</description><subject>algorithm</subject><subject>Algorithms</subject><subject>applications</subject><subject>Audio data</subject><subject>Computer vision</subject><subject>Data models</subject><subject>Deep learning</subject><subject>Generative adversarial networks</subject><subject>Generators</subject><subject>Image processing</subject><subject>Inference algorithms</subject><subject>Linear programming</subject><subject>Machine learning algorithms</subject><subject>Natural language processing</subject><subject>theory</subject><issn>1041-4347</issn><issn>1558-2191</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1PwkAQhjdGExH9AcbLJl4t7vduvTWIaCQaDZ6bZTuVInTrboHw7y2BeJr38D4zkweha0oGlJL0fvr6OBowwuiAU05oSk9Qj0ppEtbl0y4TQRPBhT5HFzEuCCFGG9pDHxn-hE0FW-xrPIYagm2rDeCs2ECINlR2id-g3frwEx9wtvz2oWrnq3iHp3PwYXeHbV3grGmWletIX8dLdFbaZYSr4-yjr6fRdPicTN7HL8NskjiWMpFowXWhuZJqVjo-0ymZCae4VqLkEhwAWMGMNKxwilkpqdTCESipMsKoUvA-uj3sbYL_XUNs84Vfh7o7mTNthJSEp7Rr0UPLBR9jgDJvQrWyYZdTku_N5Xtz-d5cfjTXMTcHpuq--O-nikmdGv4HfJZofQ</recordid><startdate>20230401</startdate><enddate>20230401</enddate><creator>Gui, Jie</creator><creator>Sun, Zhenan</creator><creator>Wen, Yonggang</creator><creator>Tao, Dacheng</creator><creator>Ye, Jieping</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0003-4029-9935</orcidid><orcidid>https://orcid.org/0000-0001-7225-5449</orcidid><orcidid>https://orcid.org/0000-0002-9450-1759</orcidid><orcidid>https://orcid.org/0000-0002-2751-5114</orcidid></search><sort><creationdate>20230401</creationdate><title>A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications</title><author>Gui, Jie ; Sun, Zhenan ; Wen, Yonggang ; Tao, Dacheng ; Ye, Jieping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2924-7437d73656bfc3b790b4c63764f35eceeea428582dc62a551574c0ef168486f43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>algorithm</topic><topic>Algorithms</topic><topic>applications</topic><topic>Audio data</topic><topic>Computer vision</topic><topic>Data models</topic><topic>Deep learning</topic><topic>Generative adversarial networks</topic><topic>Generators</topic><topic>Image processing</topic><topic>Inference algorithms</topic><topic>Linear programming</topic><topic>Machine learning algorithms</topic><topic>Natural language processing</topic><topic>theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gui, Jie</creatorcontrib><creatorcontrib>Sun, Zhenan</creatorcontrib><creatorcontrib>Wen, Yonggang</creatorcontrib><creatorcontrib>Tao, Dacheng</creatorcontrib><creatorcontrib>Ye, Jieping</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on knowledge and data engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gui, Jie</au><au>Sun, Zhenan</au><au>Wen, Yonggang</au><au>Tao, Dacheng</au><au>Ye, Jieping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications</atitle><jtitle>IEEE transactions on knowledge and data engineering</jtitle><stitle>TKDE</stitle><date>2023-04-01</date><risdate>2023</risdate><volume>35</volume><issue>4</issue><spage>3313</spage><epage>3332</epage><pages>3313-3332</pages><issn>1041-4347</issn><eissn>1558-2191</eissn><coden>ITKEEH</coden><abstract>Generative adversarial networks (GANs) have recently become a hot research topic; however, they have been studied since 2014, and a large number of algorithms have been proposed. Nevertheless, few comprehensive studies explain the connections among different GAN variants and how they have evolved. In this paper, we attempt to provide a review of the various GAN methods from the perspectives of algorithms, theory, and applications. First, the motivations, mathematical representations, and structures of most GAN algorithms are introduced in detail, and we compare their commonalities and differences. Second, theoretical issues related to GANs are investigated. Finally, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, the medical field, and data science are discussed.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TKDE.2021.3130191</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0003-4029-9935</orcidid><orcidid>https://orcid.org/0000-0001-7225-5449</orcidid><orcidid>https://orcid.org/0000-0002-9450-1759</orcidid><orcidid>https://orcid.org/0000-0002-2751-5114</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1041-4347 |
| ispartof | IEEE transactions on knowledge and data engineering, 2023-04, Vol.35 (4), p.3313-3332 |
| issn | 1041-4347 1558-2191 |
| language | eng |
| recordid | cdi_ieee_primary_9625798 |
| source | IEEE Electronic Library (IEL) |
| subjects | algorithm Algorithms applications Audio data Computer vision Data models Deep learning Generative adversarial networks Generators Image processing Inference algorithms Linear programming Machine learning algorithms Natural language processing theory |
| title | A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T13%3A36%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Review%20on%20Generative%20Adversarial%20Networks:%20Algorithms,%20Theory,%20and%20Applications&rft.jtitle=IEEE%20transactions%20on%20knowledge%20and%20data%20engineering&rft.au=Gui,%20Jie&rft.date=2023-04-01&rft.volume=35&rft.issue=4&rft.spage=3313&rft.epage=3332&rft.pages=3313-3332&rft.issn=1041-4347&rft.eissn=1558-2191&rft.coden=ITKEEH&rft_id=info:doi/10.1109/TKDE.2021.3130191&rft_dat=%3Cproquest_RIE%3E2784550391%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2784550391&rft_id=info:pmid/&rft_ieee_id=9625798&rfr_iscdi=true |