Multiple Generative Adversarial Networks Analysis for Predicting Photographers' Retouching

Anyone can take a photo, but not everybody has the ability to retouch their pictures and obtain result close to professional. Since it is not possible to ask experts to retouch thousands of pictures, we thought about teaching a piece of software how to reproduce the work of those said experts. This...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2020-06
Main Authors: Bickel, Marc, Dubuis, Samuel, Gachoud, Sébastien
Format: Article
Language:English
Subjects:
Artists
Generative adversarial networks
Machine learning
Pictures
Retouching
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Bickel, Marc
Dubuis, Samuel
Gachoud, Sébastien
description Anyone can take a photo, but not everybody has the ability to retouch their pictures and obtain result close to professional. Since it is not possible to ask experts to retouch thousands of pictures, we thought about teaching a piece of software how to reproduce the work of those said experts. This study aims to explore the possibility to use deep learning methods and more specifically, generative adversarial networks (GANs), to mimic artists' retouching and find which one of the studied models provides the best results.
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2409767596</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2409767596</sourcerecordid><originalsourceid>FETCH-proquest_journals_24097675963</originalsourceid><addsrcrecordid>eNqNjM0KgkAURocgSMp3GGjRSrDxL5cS_WyKiFZtZNCrjg2O3TsWvX0ueoBW3-Kc802YI4Jg7W1CIWbMJWp93xdxIqIocNj9NGireg38AB2gtOoFPCtfgCRRSc3PYN8GH8SzTuoPKeKVQX5BKFVhVVfzS2OsqVH2zdis-BWsGYpmJAs2raQmcH87Z8v97rY9ej2a5wBk89YMOL5SLkI_TeIkSuPgP-sLqOFDpw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2409767596</pqid></control><display><type>article</type><title>Multiple Generative Adversarial Networks Analysis for Predicting Photographers' Retouching</title><source>Publicly Available Content Database</source><creator>Bickel, Marc ; Dubuis, Samuel ; Gachoud, Sébastien</creator><creatorcontrib>Bickel, Marc ; Dubuis, Samuel ; Gachoud, Sébastien</creatorcontrib><description>Anyone can take a photo, but not everybody has the ability to retouch their pictures and obtain result close to professional. Since it is not possible to ask experts to retouch thousands of pictures, we thought about teaching a piece of software how to reproduce the work of those said experts. This study aims to explore the possibility to use deep learning methods and more specifically, generative adversarial networks (GANs), to mimic artists' retouching and find which one of the studied models provides the best results.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Artists ; Generative adversarial networks ; Machine learning ; Pictures ; Retouching</subject><ispartof>arXiv.org, 2020-06</ispartof><rights>2020. This work is published under http://creativecommons.org/licenses/by-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2409767596?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>776,780,25728,36986,44563</link.rule.ids></links><search><creatorcontrib>Bickel, Marc</creatorcontrib><creatorcontrib>Dubuis, Samuel</creatorcontrib><creatorcontrib>Gachoud, Sébastien</creatorcontrib><title>Multiple Generative Adversarial Networks Analysis for Predicting Photographers' Retouching</title><title>arXiv.org</title><description>Anyone can take a photo, but not everybody has the ability to retouch their pictures and obtain result close to professional. Since it is not possible to ask experts to retouch thousands of pictures, we thought about teaching a piece of software how to reproduce the work of those said experts. This study aims to explore the possibility to use deep learning methods and more specifically, generative adversarial networks (GANs), to mimic artists' retouching and find which one of the studied models provides the best results.</description><subject>Artists</subject><subject>Generative adversarial networks</subject><subject>Machine learning</subject><subject>Pictures</subject><subject>Retouching</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqNjM0KgkAURocgSMp3GGjRSrDxL5cS_WyKiFZtZNCrjg2O3TsWvX0ueoBW3-Kc802YI4Jg7W1CIWbMJWp93xdxIqIocNj9NGireg38AB2gtOoFPCtfgCRRSc3PYN8GH8SzTuoPKeKVQX5BKFVhVVfzS2OsqVH2zdis-BWsGYpmJAs2raQmcH87Z8v97rY9ej2a5wBk89YMOL5SLkI_TeIkSuPgP-sLqOFDpw</recordid><startdate>20200603</startdate><enddate>20200603</enddate><creator>Bickel, Marc</creator><creator>Dubuis, Samuel</creator><creator>Gachoud, Sébastien</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PIMPY</scope><scope>PKEHL</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20200603</creationdate><title>Multiple Generative Adversarial Networks Analysis for Predicting Photographers' Retouching</title><author>Bickel, Marc ; Dubuis, Samuel ; Gachoud, Sébastien</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_24097675963</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artists</topic><topic>Generative adversarial networks</topic><topic>Machine learning</topic><topic>Pictures</topic><topic>Retouching</topic><toplevel>online_resources</toplevel><creatorcontrib>Bickel, Marc</creatorcontrib><creatorcontrib>Dubuis, Samuel</creatorcontrib><creatorcontrib>Gachoud, Sébastien</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied &amp; Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bickel, Marc</au><au>Dubuis, Samuel</au><au>Gachoud, Sébastien</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Multiple Generative Adversarial Networks Analysis for Predicting Photographers' Retouching</atitle><jtitle>arXiv.org</jtitle><date>2020-06-03</date><risdate>2020</risdate><eissn>2331-8422</eissn><abstract>Anyone can take a photo, but not everybody has the ability to retouch their pictures and obtain result close to professional. Since it is not possible to ask experts to retouch thousands of pictures, we thought about teaching a piece of software how to reproduce the work of those said experts. This study aims to explore the possibility to use deep learning methods and more specifically, generative adversarial networks (GANs), to mimic artists' retouching and find which one of the studied models provides the best results.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2020-06
issn 2331-8422
language eng
recordid cdi_proquest_journals_2409767596
source Publicly Available Content Database
subjects Artists
Generative adversarial networks
Machine learning
Pictures
Retouching
title Multiple Generative Adversarial Networks Analysis for Predicting Photographers' Retouching
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-27T07%3A18%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Multiple%20Generative%20Adversarial%20Networks%20Analysis%20for%20Predicting%20Photographers'%20Retouching&rft.jtitle=arXiv.org&rft.au=Bickel,%20Marc&rft.date=2020-06-03&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2409767596%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_24097675963%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2409767596&rft_id=info:pmid/&rfr_iscdi=true