Generative Adversarial Networks based on optimal transport: a survey

Abstract Optimal transport theory provides a distance to find the cheapest way to convey an object from one place to another, based on a certain cost. Optimal transport thus defines a set of geometric tools with interesting properties in terms of coupling and correspondence between probability distr...
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Autor*in:	Kamsu-Foguem, Bernard [verfasserIn] Msouobu Gueuwou, Shester Landry Kounta, Cheick Abdoul Kadir A.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Generative adversarial network Deep learning Transfer learning Optimal transport Wasserstein distance

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Artificial intelligence review - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986, 56(2022), 7 vom: 01. Dez., Seite 6723-6773
Übergeordnetes Werk:	volume:56 ; year:2022 ; number:7 ; day:01 ; month:12 ; pages:6723-6773

Links:	Volltext

DOI / URN:	10.1007/s10462-022-10342-x

Katalog-ID:	SPR051653265

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520			\|a Abstract Optimal transport theory provides a distance to find the cheapest way to convey an object from one place to another, based on a certain cost. Optimal transport thus defines a set of geometric tools with interesting properties in terms of coupling and correspondence between probability distributions. Recent theoretical and algorithmic advances in this theory generate interesting methods for data science. Bearing this in mind, Wasserstein Generative Adversarial Networks (WGAN) make it possible to generate complex data with a high degree of realism in addition to real data which may be limited in certain contexts where their accessibility is restricted. This paper presents a literature review of recent developments in optimal transport-based data science in some practical and theoretical contexts, for solving machine learning problems. In the theoretical developments, we will appreciate the extension of WGANs coupled with conditions, autoencoders, and transfer learning. We made a critical evaluation of prevalent WGANs by synthesizing and comparing information between them to improve understanding of their respective impact. The practical context shows prominent applications in the fields of industry, health, and safety. Finally, challenges are discussed, and the conclusion presents the benefits of WGAN and prospective analyses.
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allfields	10.1007/s10462-022-10342-x doi (DE-627)SPR051653265 (SPR)s10462-022-10342-x-e DE-627 ger DE-627 rakwb eng Kamsu-Foguem, Bernard verfasserin (orcid)0000-0003-3617-3184 aut Generative Adversarial Networks based on optimal transport: a survey 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Optimal transport theory provides a distance to find the cheapest way to convey an object from one place to another, based on a certain cost. Optimal transport thus defines a set of geometric tools with interesting properties in terms of coupling and correspondence between probability distributions. Recent theoretical and algorithmic advances in this theory generate interesting methods for data science. Bearing this in mind, Wasserstein Generative Adversarial Networks (WGAN) make it possible to generate complex data with a high degree of realism in addition to real data which may be limited in certain contexts where their accessibility is restricted. This paper presents a literature review of recent developments in optimal transport-based data science in some practical and theoretical contexts, for solving machine learning problems. In the theoretical developments, we will appreciate the extension of WGANs coupled with conditions, autoencoders, and transfer learning. We made a critical evaluation of prevalent WGANs by synthesizing and comparing information between them to improve understanding of their respective impact. The practical context shows prominent applications in the fields of industry, health, and safety. Finally, challenges are discussed, and the conclusion presents the benefits of WGAN and prospective analyses. Generative adversarial network (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 Optimal transport (dpeaa)DE-He213 Wasserstein distance (dpeaa)DE-He213 Msouobu Gueuwou, Shester Landry aut Kounta, Cheick Abdoul Kadir A. aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 56(2022), 7 vom: 01. Dez., Seite 6723-6773 (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:56 year:2022 number:7 day:01 month:12 pages:6723-6773 https://dx.doi.org/10.1007/s10462-022-10342-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2022 7 01 12 6723-6773
spelling	10.1007/s10462-022-10342-x doi (DE-627)SPR051653265 (SPR)s10462-022-10342-x-e DE-627 ger DE-627 rakwb eng Kamsu-Foguem, Bernard verfasserin (orcid)0000-0003-3617-3184 aut Generative Adversarial Networks based on optimal transport: a survey 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Optimal transport theory provides a distance to find the cheapest way to convey an object from one place to another, based on a certain cost. Optimal transport thus defines a set of geometric tools with interesting properties in terms of coupling and correspondence between probability distributions. Recent theoretical and algorithmic advances in this theory generate interesting methods for data science. Bearing this in mind, Wasserstein Generative Adversarial Networks (WGAN) make it possible to generate complex data with a high degree of realism in addition to real data which may be limited in certain contexts where their accessibility is restricted. This paper presents a literature review of recent developments in optimal transport-based data science in some practical and theoretical contexts, for solving machine learning problems. In the theoretical developments, we will appreciate the extension of WGANs coupled with conditions, autoencoders, and transfer learning. We made a critical evaluation of prevalent WGANs by synthesizing and comparing information between them to improve understanding of their respective impact. The practical context shows prominent applications in the fields of industry, health, and safety. Finally, challenges are discussed, and the conclusion presents the benefits of WGAN and prospective analyses. Generative adversarial network (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 Optimal transport (dpeaa)DE-He213 Wasserstein distance (dpeaa)DE-He213 Msouobu Gueuwou, Shester Landry aut Kounta, Cheick Abdoul Kadir A. aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 56(2022), 7 vom: 01. Dez., Seite 6723-6773 (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:56 year:2022 number:7 day:01 month:12 pages:6723-6773 https://dx.doi.org/10.1007/s10462-022-10342-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2022 7 01 12 6723-6773
allfields_unstemmed	10.1007/s10462-022-10342-x doi (DE-627)SPR051653265 (SPR)s10462-022-10342-x-e DE-627 ger DE-627 rakwb eng Kamsu-Foguem, Bernard verfasserin (orcid)0000-0003-3617-3184 aut Generative Adversarial Networks based on optimal transport: a survey 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Optimal transport theory provides a distance to find the cheapest way to convey an object from one place to another, based on a certain cost. Optimal transport thus defines a set of geometric tools with interesting properties in terms of coupling and correspondence between probability distributions. Recent theoretical and algorithmic advances in this theory generate interesting methods for data science. Bearing this in mind, Wasserstein Generative Adversarial Networks (WGAN) make it possible to generate complex data with a high degree of realism in addition to real data which may be limited in certain contexts where their accessibility is restricted. This paper presents a literature review of recent developments in optimal transport-based data science in some practical and theoretical contexts, for solving machine learning problems. In the theoretical developments, we will appreciate the extension of WGANs coupled with conditions, autoencoders, and transfer learning. We made a critical evaluation of prevalent WGANs by synthesizing and comparing information between them to improve understanding of their respective impact. The practical context shows prominent applications in the fields of industry, health, and safety. Finally, challenges are discussed, and the conclusion presents the benefits of WGAN and prospective analyses. Generative adversarial network (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 Optimal transport (dpeaa)DE-He213 Wasserstein distance (dpeaa)DE-He213 Msouobu Gueuwou, Shester Landry aut Kounta, Cheick Abdoul Kadir A. aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 56(2022), 7 vom: 01. Dez., Seite 6723-6773 (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:56 year:2022 number:7 day:01 month:12 pages:6723-6773 https://dx.doi.org/10.1007/s10462-022-10342-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2022 7 01 12 6723-6773
allfieldsGer	10.1007/s10462-022-10342-x doi (DE-627)SPR051653265 (SPR)s10462-022-10342-x-e DE-627 ger DE-627 rakwb eng Kamsu-Foguem, Bernard verfasserin (orcid)0000-0003-3617-3184 aut Generative Adversarial Networks based on optimal transport: a survey 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Optimal transport theory provides a distance to find the cheapest way to convey an object from one place to another, based on a certain cost. Optimal transport thus defines a set of geometric tools with interesting properties in terms of coupling and correspondence between probability distributions. Recent theoretical and algorithmic advances in this theory generate interesting methods for data science. Bearing this in mind, Wasserstein Generative Adversarial Networks (WGAN) make it possible to generate complex data with a high degree of realism in addition to real data which may be limited in certain contexts where their accessibility is restricted. This paper presents a literature review of recent developments in optimal transport-based data science in some practical and theoretical contexts, for solving machine learning problems. In the theoretical developments, we will appreciate the extension of WGANs coupled with conditions, autoencoders, and transfer learning. We made a critical evaluation of prevalent WGANs by synthesizing and comparing information between them to improve understanding of their respective impact. The practical context shows prominent applications in the fields of industry, health, and safety. Finally, challenges are discussed, and the conclusion presents the benefits of WGAN and prospective analyses. Generative adversarial network (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 Optimal transport (dpeaa)DE-He213 Wasserstein distance (dpeaa)DE-He213 Msouobu Gueuwou, Shester Landry aut Kounta, Cheick Abdoul Kadir A. aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 56(2022), 7 vom: 01. Dez., Seite 6723-6773 (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:56 year:2022 number:7 day:01 month:12 pages:6723-6773 https://dx.doi.org/10.1007/s10462-022-10342-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2022 7 01 12 6723-6773
allfieldsSound	10.1007/s10462-022-10342-x doi (DE-627)SPR051653265 (SPR)s10462-022-10342-x-e DE-627 ger DE-627 rakwb eng Kamsu-Foguem, Bernard verfasserin (orcid)0000-0003-3617-3184 aut Generative Adversarial Networks based on optimal transport: a survey 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Optimal transport theory provides a distance to find the cheapest way to convey an object from one place to another, based on a certain cost. Optimal transport thus defines a set of geometric tools with interesting properties in terms of coupling and correspondence between probability distributions. Recent theoretical and algorithmic advances in this theory generate interesting methods for data science. Bearing this in mind, Wasserstein Generative Adversarial Networks (WGAN) make it possible to generate complex data with a high degree of realism in addition to real data which may be limited in certain contexts where their accessibility is restricted. This paper presents a literature review of recent developments in optimal transport-based data science in some practical and theoretical contexts, for solving machine learning problems. In the theoretical developments, we will appreciate the extension of WGANs coupled with conditions, autoencoders, and transfer learning. We made a critical evaluation of prevalent WGANs by synthesizing and comparing information between them to improve understanding of their respective impact. The practical context shows prominent applications in the fields of industry, health, and safety. Finally, challenges are discussed, and the conclusion presents the benefits of WGAN and prospective analyses. Generative adversarial network (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 Optimal transport (dpeaa)DE-He213 Wasserstein distance (dpeaa)DE-He213 Msouobu Gueuwou, Shester Landry aut Kounta, Cheick Abdoul Kadir A. aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 56(2022), 7 vom: 01. Dez., Seite 6723-6773 (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:56 year:2022 number:7 day:01 month:12 pages:6723-6773 https://dx.doi.org/10.1007/s10462-022-10342-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 56 2022 7 01 12 6723-6773
language	English
source	Enthalten in Artificial intelligence review 56(2022), 7 vom: 01. Dez., Seite 6723-6773 volume:56 year:2022 number:7 day:01 month:12 pages:6723-6773
sourceStr	Enthalten in Artificial intelligence review 56(2022), 7 vom: 01. Dez., Seite 6723-6773 volume:56 year:2022 number:7 day:01 month:12 pages:6723-6773
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Generative adversarial network Deep learning Transfer learning Optimal transport Wasserstein distance
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container_title	Artificial intelligence review
authorswithroles_txt_mv	Kamsu-Foguem, Bernard @@aut@@ Msouobu Gueuwou, Shester Landry @@aut@@ Kounta, Cheick Abdoul Kadir A. @@aut@@
publishDateDaySort_date	2022-12-01T00:00:00Z
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author	Kamsu-Foguem, Bernard
spellingShingle	Kamsu-Foguem, Bernard misc Generative adversarial network misc Deep learning misc Transfer learning misc Optimal transport misc Wasserstein distance Generative Adversarial Networks based on optimal transport: a survey
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topic_title	Generative Adversarial Networks based on optimal transport: a survey Generative adversarial network (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 Optimal transport (dpeaa)DE-He213 Wasserstein distance (dpeaa)DE-He213
topic	misc Generative adversarial network misc Deep learning misc Transfer learning misc Optimal transport misc Wasserstein distance
topic_unstemmed	misc Generative adversarial network misc Deep learning misc Transfer learning misc Optimal transport misc Wasserstein distance
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title	Generative Adversarial Networks based on optimal transport: a survey
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title_full	Generative Adversarial Networks based on optimal transport: a survey
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author_browse	Kamsu-Foguem, Bernard Msouobu Gueuwou, Shester Landry Kounta, Cheick Abdoul Kadir A.
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title_sort	generative adversarial networks based on optimal transport: a survey
title_auth	Generative Adversarial Networks based on optimal transport: a survey
abstract	Abstract Optimal transport theory provides a distance to find the cheapest way to convey an object from one place to another, based on a certain cost. Optimal transport thus defines a set of geometric tools with interesting properties in terms of coupling and correspondence between probability distributions. Recent theoretical and algorithmic advances in this theory generate interesting methods for data science. Bearing this in mind, Wasserstein Generative Adversarial Networks (WGAN) make it possible to generate complex data with a high degree of realism in addition to real data which may be limited in certain contexts where their accessibility is restricted. This paper presents a literature review of recent developments in optimal transport-based data science in some practical and theoretical contexts, for solving machine learning problems. In the theoretical developments, we will appreciate the extension of WGANs coupled with conditions, autoencoders, and transfer learning. We made a critical evaluation of prevalent WGANs by synthesizing and comparing information between them to improve understanding of their respective impact. The practical context shows prominent applications in the fields of industry, health, and safety. Finally, challenges are discussed, and the conclusion presents the benefits of WGAN and prospective analyses. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Optimal transport theory provides a distance to find the cheapest way to convey an object from one place to another, based on a certain cost. Optimal transport thus defines a set of geometric tools with interesting properties in terms of coupling and correspondence between probability distributions. Recent theoretical and algorithmic advances in this theory generate interesting methods for data science. Bearing this in mind, Wasserstein Generative Adversarial Networks (WGAN) make it possible to generate complex data with a high degree of realism in addition to real data which may be limited in certain contexts where their accessibility is restricted. This paper presents a literature review of recent developments in optimal transport-based data science in some practical and theoretical contexts, for solving machine learning problems. In the theoretical developments, we will appreciate the extension of WGANs coupled with conditions, autoencoders, and transfer learning. We made a critical evaluation of prevalent WGANs by synthesizing and comparing information between them to improve understanding of their respective impact. The practical context shows prominent applications in the fields of industry, health, and safety. Finally, challenges are discussed, and the conclusion presents the benefits of WGAN and prospective analyses. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Optimal transport theory provides a distance to find the cheapest way to convey an object from one place to another, based on a certain cost. Optimal transport thus defines a set of geometric tools with interesting properties in terms of coupling and correspondence between probability distributions. Recent theoretical and algorithmic advances in this theory generate interesting methods for data science. Bearing this in mind, Wasserstein Generative Adversarial Networks (WGAN) make it possible to generate complex data with a high degree of realism in addition to real data which may be limited in certain contexts where their accessibility is restricted. This paper presents a literature review of recent developments in optimal transport-based data science in some practical and theoretical contexts, for solving machine learning problems. In the theoretical developments, we will appreciate the extension of WGANs coupled with conditions, autoencoders, and transfer learning. We made a critical evaluation of prevalent WGANs by synthesizing and comparing information between them to improve understanding of their respective impact. The practical context shows prominent applications in the fields of industry, health, and safety. Finally, challenges are discussed, and the conclusion presents the benefits of WGAN and prospective analyses. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Generative Adversarial Networks based on optimal transport: a survey
url	https://dx.doi.org/10.1007/s10462-022-10342-x
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author2	Msouobu Gueuwou, Shester Landry Kounta, Cheick Abdoul Kadir A.
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doi_str	10.1007/s10462-022-10342-x
up_date	2024-07-03T23:04:51.143Z
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Optimal transport theory provides a distance to find the cheapest way to convey an object from one place to another, based on a certain cost. Optimal transport thus defines a set of geometric tools with interesting properties in terms of coupling and correspondence between probability distributions. Recent theoretical and algorithmic advances in this theory generate interesting methods for data science. Bearing this in mind, Wasserstein Generative Adversarial Networks (WGAN) make it possible to generate complex data with a high degree of realism in addition to real data which may be limited in certain contexts where their accessibility is restricted. This paper presents a literature review of recent developments in optimal transport-based data science in some practical and theoretical contexts, for solving machine learning problems. In the theoretical developments, we will appreciate the extension of WGANs coupled with conditions, autoencoders, and transfer learning. We made a critical evaluation of prevalent WGANs by synthesizing and comparing information between them to improve understanding of their respective impact. The practical context shows prominent applications in the fields of industry, health, and safety. 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