Generative Adversarial Networks in the Geosciences

Generative Adversarial Networks in the Geosciences. Mateo-García, G., Laparra, V., Requena-Mesa, C., & Gómez-Chova, L. In Deep learning for the Earth Sciences, pages 24–36. John Wiley & Sons, Ltd, 2021. Section: 3 _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119646181.ch3

Paper doi abstract bibtex

One of the most exciting trends in machine learning nowadays is the use of deep networks to learn density properties. In this direction, the field of generative models has become a hot topic in deep learning, and different approaches have been proposed. In this chapter, we are going to overview some of the generative models based on deep learning and focus on one of the most used ones: the Generative Adversarial Networks (GANs). We review the main different families of GANs, and how these families have been applied to remote sensing problems. Finally, in the last section, we illustrate the use of GANs in remote sensing problems with two different applications.

@incollection{mateo-garcia_generative_2021,
	title = {Generative {Adversarial} {Networks} in the {Geosciences}},
	isbn = {978-1-119-64618-1},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119646181.ch3},
	abstract = {One of the most exciting trends in machine learning nowadays is the use of deep networks to learn density properties. In this direction, the field of generative models has become a hot topic in deep learning, and different approaches have been proposed. In this chapter, we are going to overview some of the generative models based on deep learning and focus on one of the most used ones: the Generative Adversarial Networks (GANs). We review the main different families of GANs, and how these families have been applied to remote sensing problems. Finally, in the last section, we illustrate the use of GANs in remote sensing problems with two different applications.},
	language = {en},
	urldate = {2021-08-28},
	booktitle = {Deep learning for the {Earth} {Sciences}},
	publisher = {John Wiley \& Sons, Ltd},
	author = {Mateo-García, Gonzalo and Laparra, Valero and Requena-Mesa, Christian and Gómez-Chova, Luis},
	year = {2021},
	doi = {10.1002/9781119646181.ch3},
	note = {Section: 3
\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119646181.ch3},
	keywords = {Earth observation images, deep learning, generative adversarial networks, geosciences applications, remote sensing},
	pages = {24--36},
}

Downloads: 0

{"_id":"WLsHJbbkxigPgus6e","bibbaseid":"mateogarca-laparra-requenamesa-gmezchova-generativeadversarialnetworksinthegeosciences-2021","author_short":["Mateo-García, G.","Laparra, V.","Requena-Mesa, C.","Gómez-Chova, L."],"bibdata":{"bibtype":"incollection","type":"incollection","title":"Generative Adversarial Networks in the Geosciences","isbn":"978-1-119-64618-1","url":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119646181.ch3","abstract":"One of the most exciting trends in machine learning nowadays is the use of deep networks to learn density properties. In this direction, the field of generative models has become a hot topic in deep learning, and different approaches have been proposed. In this chapter, we are going to overview some of the generative models based on deep learning and focus on one of the most used ones: the Generative Adversarial Networks (GANs). We review the main different families of GANs, and how these families have been applied to remote sensing problems. Finally, in the last section, we illustrate the use of GANs in remote sensing problems with two different applications.","language":"en","urldate":"2021-08-28","booktitle":"Deep learning for the Earth Sciences","publisher":"John Wiley & Sons, Ltd","author":[{"propositions":[],"lastnames":["Mateo-García"],"firstnames":["Gonzalo"],"suffixes":[]},{"propositions":[],"lastnames":["Laparra"],"firstnames":["Valero"],"suffixes":[]},{"propositions":[],"lastnames":["Requena-Mesa"],"firstnames":["Christian"],"suffixes":[]},{"propositions":[],"lastnames":["Gómez-Chova"],"firstnames":["Luis"],"suffixes":[]}],"year":"2021","doi":"10.1002/9781119646181.ch3","note":"Section: 3 _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119646181.ch3","keywords":"Earth observation images, deep learning, generative adversarial networks, geosciences applications, remote sensing","pages":"24–36","bibtex":"@incollection{mateo-garcia_generative_2021,\n\ttitle = {Generative {Adversarial} {Networks} in the {Geosciences}},\n\tisbn = {978-1-119-64618-1},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119646181.ch3},\n\tabstract = {One of the most exciting trends in machine learning nowadays is the use of deep networks to learn density properties. In this direction, the field of generative models has become a hot topic in deep learning, and different approaches have been proposed. In this chapter, we are going to overview some of the generative models based on deep learning and focus on one of the most used ones: the Generative Adversarial Networks (GANs). We review the main different families of GANs, and how these families have been applied to remote sensing problems. Finally, in the last section, we illustrate the use of GANs in remote sensing problems with two different applications.},\n\tlanguage = {en},\n\turldate = {2021-08-28},\n\tbooktitle = {Deep learning for the {Earth} {Sciences}},\n\tpublisher = {John Wiley \\& Sons, Ltd},\n\tauthor = {Mateo-García, Gonzalo and Laparra, Valero and Requena-Mesa, Christian and Gómez-Chova, Luis},\n\tyear = {2021},\n\tdoi = {10.1002/9781119646181.ch3},\n\tnote = {Section: 3\n\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119646181.ch3},\n\tkeywords = {Earth observation images, deep learning, generative adversarial networks, geosciences applications, remote sensing},\n\tpages = {24--36},\n}\n\n","author_short":["Mateo-García, G.","Laparra, V.","Requena-Mesa, C.","Gómez-Chova, L."],"key":"mateo-garcia_generative_2021","id":"mateo-garcia_generative_2021","bibbaseid":"mateogarca-laparra-requenamesa-gmezchova-generativeadversarialnetworksinthegeosciences-2021","role":"author","urls":{"Paper":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119646181.ch3"},"keyword":["Earth observation images","deep learning","generative adversarial networks","geosciences applications","remote sensing"],"metadata":{"authorlinks":{}}},"bibtype":"incollection","biburl":"https://bibbase.org/zotero/manabsaharia","dataSources":["XpugPdSrCaPJgR6v7"],"keywords":["earth observation images","deep learning","generative adversarial networks","geosciences applications","remote sensing"],"search_terms":["generative","adversarial","networks","geosciences","mateo-garcía","laparra","requena-mesa","gómez-chova"],"title":"Generative Adversarial Networks in the Geosciences","year":2021}