Desarrollo de un prototipo de software de generación de bocetos miniatura con redes neuronales

Los bocetos miniatura thumbnails son imágenes sencillas usadas en el área de arte conceptual digital con la finalidad de construir, diseñar o ejecutar una idea, antes de iniciar la producción de la imagen final. Este proceso ayuda a un artista a tener un mejor concepto de las ideas que quiere plasma...

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Autores:
Diaz Pinilla, Sergio Alejandro
Tipo de recurso:
Fecha de publicación:
2020
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/79379
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/79379
Palabra clave:
000 - Ciencias de la computación, información y obras generales::003 - Sistemas
Bocetos Miniatura
Redes Neuronales
GAN
Generación de Arte
Thumbnail Art
Neural Network
Art Generation
Rights
openAccess
License
Atribución-CompartirIgual 4.0 Internacional
id UNACIONAL2_f7e350a6e83612ea9a90d49ae0b9208c
oai_identifier_str oai:repositorio.unal.edu.co:unal/79379
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Desarrollo de un prototipo de software de generación de bocetos miniatura con redes neuronales
dc.title.translated.none.fl_str_mv Development of a software prototype forThumbnail generation with neural networks
title Desarrollo de un prototipo de software de generación de bocetos miniatura con redes neuronales
spellingShingle Desarrollo de un prototipo de software de generación de bocetos miniatura con redes neuronales
000 - Ciencias de la computación, información y obras generales::003 - Sistemas
Bocetos Miniatura
Redes Neuronales
GAN
Generación de Arte
Thumbnail Art
Neural Network
Art Generation
title_short Desarrollo de un prototipo de software de generación de bocetos miniatura con redes neuronales
title_full Desarrollo de un prototipo de software de generación de bocetos miniatura con redes neuronales
title_fullStr Desarrollo de un prototipo de software de generación de bocetos miniatura con redes neuronales
title_full_unstemmed Desarrollo de un prototipo de software de generación de bocetos miniatura con redes neuronales
title_sort Desarrollo de un prototipo de software de generación de bocetos miniatura con redes neuronales
dc.creator.fl_str_mv Diaz Pinilla, Sergio Alejandro
dc.contributor.advisor.none.fl_str_mv Niño Vasquez, Luis Fernando
dc.contributor.author.none.fl_str_mv Diaz Pinilla, Sergio Alejandro
dc.contributor.researchgroup.spa.fl_str_mv LABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISI
dc.subject.ddc.spa.fl_str_mv 000 - Ciencias de la computación, información y obras generales::003 - Sistemas
topic 000 - Ciencias de la computación, información y obras generales::003 - Sistemas
Bocetos Miniatura
Redes Neuronales
GAN
Generación de Arte
Thumbnail Art
Neural Network
Art Generation
dc.subject.proposal.spa.fl_str_mv Bocetos Miniatura
Redes Neuronales
GAN
Generación de Arte
dc.subject.proposal.eng.fl_str_mv Thumbnail Art
Neural Network
Art Generation
description Los bocetos miniatura thumbnails son imágenes sencillas usadas en el área de arte conceptual digital con la finalidad de construir, diseñar o ejecutar una idea, antes de iniciar la producción de la imagen final. Este proceso ayuda a un artista a tener un mejor concepto de las ideas que quiere plasmar, visualizar ideas y descartar elementos que pueden no funcionar. Este proceso permite la iteración rápida de ideas, por lo que es una herramienta muy útil para los artistas en su proceso creativo. El objetivo principal de este trabajo fue desarrollar un prototipo de software que mediante redes neuronales sea capaz de generar bocetos miniatura. Se consultó en la literatura las redes neuronales usadas para la tarea de generación de imágenes, que dominios de imágenes se han usado y los métodos que se usan para evaluar el desempeño. Por consiguiente, mediante el uso de una metodología general basada en aprendizaje de máquina se seleccionó y entrenó una red neuronal con el objetivo de generar estos bocetos. Durante este proceso se construyó un conjunto de datos de bocetos, se seleccionó la red neuronal StyleGAN para el proceso de entrenamiento, también se evaluó si los bocetos generados por la red cumplían con los criterios de calidad usando métricas encontradas en la literatura existente sobre este tema. Finalmente, se implementó un aplicativo que permite generar bocetos desde una página web haciendo uso de la red entrenada.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-04-06T14:51:29Z
dc.date.available.none.fl_str_mv 2021-04-06T14:51:29Z
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/79379
url https://repositorio.unal.edu.co/handle/unal/79379
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv Areeyapinan, Jennisa ; Kanongchaiyos, Pizzanu: Face morphing using critical point filters. En: JCSSE 2012 - 9th International Joint Conference on Computer Science and Software Engineering (2012), p. 283–288. ISBN 9781467319218
Assa, Jackie ; Cohen-Or, Daniel: More of the same: Synthesizing a variety by structural layering. En: Computers and Graphics (Pergamon) 36 (2012), Nr. 4, p. 250–256. ISSN 00978493
Bau, David ; Zhu, Jun-Yan ; Strobelt, Hendrik ; Zhou, Bolei ; Tenenbaum, Joshua B. ; Freeman, William T. ; Torralba, Antonio: GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. (2018)
Bergmann, Urs ; Jetchev, Nikolay ; Vollgraf, Roland: Learning Texture Manifolds with the Periodic Spatial GAN. (2017), may
Brock, Andrew ; Donahue, Jeff ; Simonyan, Karen: Large Scale GAN Training for High Fidelity Natural Image Synthesis. (2018), sep, p. 1–35
Cai, Lei ; Wang, Zhengyang ; Gao, Hongyang ; Shen, Dinggang ; Ji, Shuiwang: Deep Adversarial Learning for Multi-Modality Missing Data Completion. En: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD ’18. New York, New York, USA : ACM Press, jul 2018. – ISBN 9781450355520, p. 1158–1166
Chowdhury, S. M. A. K. ; Lubna, J. I.: Review on Deep Fake: A looming Technological Threat. En: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2020, p. 1–7
Dirvanauskas ; Maskeliunas ¯ ; Raudonis ; Damaˇsevicius ˇ ; Scherer: HEMIGEN: Human Embryo Image Generator Based on Generative Adversarial Networks. En: Sensors 19 (2019), aug, Nr. 16, p. 3578
Dong, Hao ; Zhang, Jingqing ; McIlwraith, Douglas ; Guo, Yike: I2T2I: Learning text to image synthesis with textual data augmentation. En: 2017 IEEE International Conference on Image Processing (ICIP) Vol. 2017-Septe, IEEE, sep 2017. – ISBN 9781–5090–2175–8, p. 2015–2019
Dong, Hao ; Zhang, Jingqing ; McIlwraith, Douglas ; Guo, Yike: I2T2I: Learning text to image synthesis with textual data augmentation. En: Proceedings - International Conference on Image Processing, ICIP 2017-Septe (2018), p. 2015–2019. – ISBN 9781509021758
Du, Changde ; Du, Changying ; Huang, Lijie ; He, Huiguang: Reconstructing Perceived Images From Human Brain Activities With Bayesian Deep Multiview Learning. En: IEEE Transactions on Neural Networks and Learning Systems 30 (2018), Nr. 8, p. 2310–2323. – ISSN 21622388
Li, Chuan ; Wand, Michael: Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis. (2016), jan
Li, Ming ; Ye, Chunyang ; Li, Wei: High-Resolution Network for Photorealistic Style Transfer. (2019), apr, Nr. 2001, p. 1–14
Lucic, Mario ; Kurach, Karol ; Michalski, Marcin ; Bousquet, Olivier ; Gelly, Sylvain: Are Gans created equal? A large-scale study. En: Advances in Neural Information Processing Systems 2018-December (2018), Nr. Nips, p. 700–709. – ISSN 10495258
Lucic, Mario ; Tschannen, Michael ; Ritter, Marvin ; Zhai, Xiaohua ; Bachem, Olivier ; Gelly, Sylvain: High-Fidelity Image Generation With Fewer Labels. (2019), mar
Ly, Nam T. ; Nguyen, Cuong T. ; Nakagawa, Masaki: Training an End-to-End Model for Offline Handwritten Japanese Text Recognition by Generated Synthetic Patterns. En: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR) Vol. 2018-Augus, IEEE, aug 2018. – ISBN 978–1–5386–5875–8, p. 74–79
Malik, Abid ; Lu, Micheal ; Wang, Nathenial ; Lin, Yeiwei ; Yoo, Shinjae: Detailed Performance Analysis of Distributed Tensorflow on a GPU Cluster using Deep Learning Algorithms. En: 2018 New York Scientific Data Summit (NYSDS), IEEE, aug 2018. – ISBN 978–1–5386–7933–3, p. 1–8
Mansimov, Elman ; Parisotto, Emilio ; Ba, Jimmy L. ; Salakhutdinov, Ruslan: Generating Images from Captions with Attention. (2015), p. 1–12 62 Bibliografía
Mao, Xudong ; Li, Qing: Unpaired Multi-Domain Image Generation via Regularized Conditional GANs. En: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. California : International Joint Conferences on Artificial Intelligence Organization, jul 2018. – ISBN 9780999241127, p. 2553–2559
Miyauchi, Yutaro ; Sugano, Yusuke ; Matsushita, Yasuyuki: Shape-Conditioned Image Generation by Learning Latent Appearance Representation from Unpaired Data. En: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11366 LNCS. Springer Verlag, 2019. – ISBN 9783030208752, p. 438–453
Mizginov, V. A. ; Danilov, S. Y.: Synthetic thermal background and object texture generation using geometric information and GaN. En: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives Vol. 42, International Society for Photogrammetry and Remote Sensing, 2019. – ISSN 16821750, p. 149–154
Park, Taesung ; Liu, Ming-Yu ; Wang, Ting-chun ; Zhu, Jun-yan: Semantic Image Synthesis with Spatially-Adaptive Normalization. (2019), mar
Robertson, Scott ; Bertling, Thomas: How To draw. Desing Studio, 2013
Sangkloy, Patsorn ; Burnell, Nathan ; Ham, Cusuh ; Hays, James: The sketchy database. En: ACM Transactions on Graphics 35 (2016), Nr. 4, p. 1–12. – ISSN 07300301
Sannidhan, M. S. ; Ananth Prabhu, G. ; Robbins, David E. ; Shasky, Charles: Evaluating the performance of face sketch generation using generative adversarial networks. En: Pattern Recognition Letters 128 (2019), p. 452–458. – ISSN 01678655
Siarohin, Aliaksandr ; Sangineto, Enver ; Lathuiliere, Stephane ; Sebe, Nicu: Deformable GANs for Pose-based Human Image Generation. En: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2017), dec, p. 3408–3416. – ISBN 9781538664209
Simonyan, Karen ; Zisserman, Andrew. Very Deep Convolutional Networks for LargeScale Image Recognition. 2015
Talukdar, J. ; Gupta, S. ; Rajpura, P. S. ; Hegde, R. S.: Transfer Learning for Object Detection using State-of-the-Art Deep Neural Networks. En: 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, feb 2018. – ISBN 978–1–5386–3045–7, p. 78–83 Bibliograf´ıa 63
Tan, Wei R. ; Chan, Chee S. ; Aguirre, Hernan ; Tanaka, Kiyoshi: Improved ArtGAN for Conditional Synthesis of Natural Image and Artwork. (2017), aug, p. 1–16
Theagarajan, Rajkumar ; Bhanu, Bir: DeepHesc 2.0: Deep generative multi adversarial networks for improving the classification of hESC. En: PLoS ONE 14 (2019), mar, Nr. 3. – ISSN 19326203
Ulyanov, Dmitry ; Vedaldi, Andrea ; Lempitsky, Victor: Instance Normalization: The Missing Ingredient for Fast Stylization. (2016), jul, Nr. 2016
Wang, Haohan ; Raj, Bhiksha: On the Origin of Deep Learning. (2017), p. 1–72
Wu, Huikai ; Zheng, Shuai ; Zhang, Junge ; Huang, Kaiqi: GP-GAN: Towards Realistic High-Resolution Image Blending. (2017), mar. ISBN 9781450368896
Xie, Ning ; Hachiya, Hirotaka ; Sugiyama, Masashi: Artist agent: A reinforcement learning approach to automatic stroke generation in oriental ink painting. En: IEICE Transactions on Information and Systems E96-D (2013), Nr. 5, p. 1134–1144. – ISBN 9781450312851
Xie, Weidi ; Noble, J. A. ; Zisserman, Andrew: Microscopy cell counting and detection with fully convolutional regression networks. En: Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization 6 (2018), may, Nr. 3, p. 283–292. – ISSN 21681171
Yang, Wei ; Ouyang, Wanli ; Wang, Xiaolong ; Ren, Jimmy ; Li, Hongsheng ;
Wang, Xiaogang: 3D Human Pose Estimation in the Wild by Adversarial Learning. En: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018), p. 5255–5264. – ISBN 9781538664209
Yu, Bingqing ; Clark, James J.: WAYLA - Generating Images from Eye Movements. En: 2018 15th Conference on Computer and Robot Vision (CRV), IEEE, may 2018. – ISBN 978–1–5386–6481–0, p. 118–125
Zha, Xuewei ; Shi, Fei ; Ma, Yuhui ; Zhu, Weifang ; Chen, Xinjian: Generation of retinal OCT images with diseases based on cGAN. En: Angelini, Elsa D. (Ed.) ; Landman, Bennett A. (Ed.): Medical Imaging 2019: Image Processing, SPIE, mar 2019. – ISBN 9781510625457, p. 74
Zhang, Zhifei ; Song, Yang ; Qi, Hairong: Decoupled Learning for Conditional Adversarial Networks. En: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) Vol. 2018-Janua, IEEE, mar 2018. – ISBN 978–1–5386–4886–5, p. 700–708 64 Bibliografía
Zhao, Bo ; Meng, Lili ; Yin, Weidong ; Sigal, Leonid: Image Generation from Layout. (2018), nov
Zhu, Jun-Yan ; Park, Taesung ; Isola, Phillip ; Efros, Alexei A.: Unpaired Imageto-Image Translation Using Cycle-Consistent Adversarial Networks. En: 2017 IEEE International Conference on Computer Vision (ICCV) Vol. 2017-Octob, IEEE, oct 2017. – ISBN 978–1–5386–1032–9, p. 2242–2251
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
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dc.format.extent.spa.fl_str_mv 1 recurso en línea (81 páginas)
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisher.department.spa.fl_str_mv Departamento de Ingeniería de Sistemas e Industrial
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingeniería
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá
institution Universidad Nacional de Colombia
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spelling Atribución-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Niño Vasquez, Luis Fernando529ee5e1893682de94fcec58bfe1f82bDiaz Pinilla, Sergio Alejandro8dd085ea1df8a142c50061f293045b94LABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISI2021-04-06T14:51:29Z2021-04-06T14:51:29Z2020https://repositorio.unal.edu.co/handle/unal/79379Los bocetos miniatura thumbnails son imágenes sencillas usadas en el área de arte conceptual digital con la finalidad de construir, diseñar o ejecutar una idea, antes de iniciar la producción de la imagen final. Este proceso ayuda a un artista a tener un mejor concepto de las ideas que quiere plasmar, visualizar ideas y descartar elementos que pueden no funcionar. Este proceso permite la iteración rápida de ideas, por lo que es una herramienta muy útil para los artistas en su proceso creativo. El objetivo principal de este trabajo fue desarrollar un prototipo de software que mediante redes neuronales sea capaz de generar bocetos miniatura. Se consultó en la literatura las redes neuronales usadas para la tarea de generación de imágenes, que dominios de imágenes se han usado y los métodos que se usan para evaluar el desempeño. Por consiguiente, mediante el uso de una metodología general basada en aprendizaje de máquina se seleccionó y entrenó una red neuronal con el objetivo de generar estos bocetos. Durante este proceso se construyó un conjunto de datos de bocetos, se seleccionó la red neuronal StyleGAN para el proceso de entrenamiento, también se evaluó si los bocetos generados por la red cumplían con los criterios de calidad usando métricas encontradas en la literatura existente sobre este tema. Finalmente, se implementó un aplicativo que permite generar bocetos desde una página web haciendo uso de la red entrenada.Thumbnails are simple images used in the area of digital conceptual art in order to build, design or execute an idea, before starting the production of the final image. This process helps the artist to have a better concept of the ideas they want to capture, visualize ideas that may or may not work, and discard elements that may not work. This process allows for the rapid iteration of ideas, making it a very useful tool for artists in their creative process. The main goal of this work was to develop a software prototype that through neural networks is capable of generating miniature sketches. The neural networks used for the imaging task, which image domains have been used, and the methods used to evaluate performance were reviewd from the literature. Therefore, through the use of a general methodology based on machine learning, a neural network type was selected and trained in order to generate such sketches. During this process, we built a set of sketch data, StyleGAN was selected for the training process, also, it was so evaluated whether the sketches generated by the network met the quality criteria using found metrics. Finally, a software application was implemented which allows the generation of sketches through a web page using the trained network.MaestríaMetodología general basada en aprendizaje de máquina se seleccionó y entrenó una red neuronal con el objetivo de generar estos bocetos. Durante este proceso se construyó un conjunto de datos de bocetos, se seleccionó la red neuronal StyleGAN para el proceso de entrenamiento, también se evaluó si los bocetos generados por la red cumplían con los criterios de calidad usando métricas encontradas en la literatura existente sobre este tema. Finalmente, se implementó un aplicativo que permite generar bocetos desde una página web haciendo uso de la red entrenada.Sistemas Inteligentes1 recurso en línea (81 páginas)application/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::003 - SistemasBocetos MiniaturaRedes NeuronalesGANGeneración de ArteThumbnail ArtNeural NetworkArt GenerationDesarrollo de un prototipo de software de generación de bocetos miniatura con redes neuronalesDevelopment of a software prototype forThumbnail generation with neural networksTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAreeyapinan, Jennisa ; Kanongchaiyos, Pizzanu: Face morphing using critical point filters. En: JCSSE 2012 - 9th International Joint Conference on Computer Science and Software Engineering (2012), p. 283–288. ISBN 9781467319218Assa, Jackie ; Cohen-Or, Daniel: More of the same: Synthesizing a variety by structural layering. En: Computers and Graphics (Pergamon) 36 (2012), Nr. 4, p. 250–256. ISSN 00978493Bau, David ; Zhu, Jun-Yan ; Strobelt, Hendrik ; Zhou, Bolei ; Tenenbaum, Joshua B. ; Freeman, William T. ; Torralba, Antonio: GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. (2018)Bergmann, Urs ; Jetchev, Nikolay ; Vollgraf, Roland: Learning Texture Manifolds with the Periodic Spatial GAN. (2017), mayBrock, Andrew ; Donahue, Jeff ; Simonyan, Karen: Large Scale GAN Training for High Fidelity Natural Image Synthesis. (2018), sep, p. 1–35Cai, Lei ; Wang, Zhengyang ; Gao, Hongyang ; Shen, Dinggang ; Ji, Shuiwang: Deep Adversarial Learning for Multi-Modality Missing Data Completion. En: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD ’18. New York, New York, USA : ACM Press, jul 2018. – ISBN 9781450355520, p. 1158–1166Chowdhury, S. M. A. K. ; Lubna, J. I.: Review on Deep Fake: A looming Technological Threat. En: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2020, p. 1–7Dirvanauskas ; Maskeliunas ¯ ; Raudonis ; Damaˇsevicius ˇ ; Scherer: HEMIGEN: Human Embryo Image Generator Based on Generative Adversarial Networks. En: Sensors 19 (2019), aug, Nr. 16, p. 3578Dong, Hao ; Zhang, Jingqing ; McIlwraith, Douglas ; Guo, Yike: I2T2I: Learning text to image synthesis with textual data augmentation. En: 2017 IEEE International Conference on Image Processing (ICIP) Vol. 2017-Septe, IEEE, sep 2017. – ISBN 9781–5090–2175–8, p. 2015–2019Dong, Hao ; Zhang, Jingqing ; McIlwraith, Douglas ; Guo, Yike: I2T2I: Learning text to image synthesis with textual data augmentation. En: Proceedings - International Conference on Image Processing, ICIP 2017-Septe (2018), p. 2015–2019. – ISBN 9781509021758Du, Changde ; Du, Changying ; Huang, Lijie ; He, Huiguang: Reconstructing Perceived Images From Human Brain Activities With Bayesian Deep Multiview Learning. En: IEEE Transactions on Neural Networks and Learning Systems 30 (2018), Nr. 8, p. 2310–2323. – ISSN 21622388Li, Chuan ; Wand, Michael: Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis. (2016), janLi, Ming ; Ye, Chunyang ; Li, Wei: High-Resolution Network for Photorealistic Style Transfer. (2019), apr, Nr. 2001, p. 1–14Lucic, Mario ; Kurach, Karol ; Michalski, Marcin ; Bousquet, Olivier ; Gelly, Sylvain: Are Gans created equal? A large-scale study. En: Advances in Neural Information Processing Systems 2018-December (2018), Nr. Nips, p. 700–709. – ISSN 10495258Lucic, Mario ; Tschannen, Michael ; Ritter, Marvin ; Zhai, Xiaohua ; Bachem, Olivier ; Gelly, Sylvain: High-Fidelity Image Generation With Fewer Labels. (2019), marLy, Nam T. ; Nguyen, Cuong T. ; Nakagawa, Masaki: Training an End-to-End Model for Offline Handwritten Japanese Text Recognition by Generated Synthetic Patterns. En: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR) Vol. 2018-Augus, IEEE, aug 2018. – ISBN 978–1–5386–5875–8, p. 74–79Malik, Abid ; Lu, Micheal ; Wang, Nathenial ; Lin, Yeiwei ; Yoo, Shinjae: Detailed Performance Analysis of Distributed Tensorflow on a GPU Cluster using Deep Learning Algorithms. En: 2018 New York Scientific Data Summit (NYSDS), IEEE, aug 2018. – ISBN 978–1–5386–7933–3, p. 1–8Mansimov, Elman ; Parisotto, Emilio ; Ba, Jimmy L. ; Salakhutdinov, Ruslan: Generating Images from Captions with Attention. (2015), p. 1–12 62 BibliografíaMao, Xudong ; Li, Qing: Unpaired Multi-Domain Image Generation via Regularized Conditional GANs. En: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. California : International Joint Conferences on Artificial Intelligence Organization, jul 2018. – ISBN 9780999241127, p. 2553–2559Miyauchi, Yutaro ; Sugano, Yusuke ; Matsushita, Yasuyuki: Shape-Conditioned Image Generation by Learning Latent Appearance Representation from Unpaired Data. En: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11366 LNCS. Springer Verlag, 2019. – ISBN 9783030208752, p. 438–453Mizginov, V. A. ; Danilov, S. Y.: Synthetic thermal background and object texture generation using geometric information and GaN. En: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives Vol. 42, International Society for Photogrammetry and Remote Sensing, 2019. – ISSN 16821750, p. 149–154Park, Taesung ; Liu, Ming-Yu ; Wang, Ting-chun ; Zhu, Jun-yan: Semantic Image Synthesis with Spatially-Adaptive Normalization. (2019), marRobertson, Scott ; Bertling, Thomas: How To draw. Desing Studio, 2013Sangkloy, Patsorn ; Burnell, Nathan ; Ham, Cusuh ; Hays, James: The sketchy database. En: ACM Transactions on Graphics 35 (2016), Nr. 4, p. 1–12. – ISSN 07300301Sannidhan, M. S. ; Ananth Prabhu, G. ; Robbins, David E. ; Shasky, Charles: Evaluating the performance of face sketch generation using generative adversarial networks. En: Pattern Recognition Letters 128 (2019), p. 452–458. – ISSN 01678655Siarohin, Aliaksandr ; Sangineto, Enver ; Lathuiliere, Stephane ; Sebe, Nicu: Deformable GANs for Pose-based Human Image Generation. En: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2017), dec, p. 3408–3416. – ISBN 9781538664209Simonyan, Karen ; Zisserman, Andrew. Very Deep Convolutional Networks for LargeScale Image Recognition. 2015Talukdar, J. ; Gupta, S. ; Rajpura, P. S. ; Hegde, R. S.: Transfer Learning for Object Detection using State-of-the-Art Deep Neural Networks. En: 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, feb 2018. – ISBN 978–1–5386–3045–7, p. 78–83 Bibliograf´ıa 63Tan, Wei R. ; Chan, Chee S. ; Aguirre, Hernan ; Tanaka, Kiyoshi: Improved ArtGAN for Conditional Synthesis of Natural Image and Artwork. (2017), aug, p. 1–16Theagarajan, Rajkumar ; Bhanu, Bir: DeepHesc 2.0: Deep generative multi adversarial networks for improving the classification of hESC. En: PLoS ONE 14 (2019), mar, Nr. 3. – ISSN 19326203Ulyanov, Dmitry ; Vedaldi, Andrea ; Lempitsky, Victor: Instance Normalization: The Missing Ingredient for Fast Stylization. (2016), jul, Nr. 2016Wang, Haohan ; Raj, Bhiksha: On the Origin of Deep Learning. (2017), p. 1–72Wu, Huikai ; Zheng, Shuai ; Zhang, Junge ; Huang, Kaiqi: GP-GAN: Towards Realistic High-Resolution Image Blending. (2017), mar. ISBN 9781450368896Xie, Ning ; Hachiya, Hirotaka ; Sugiyama, Masashi: Artist agent: A reinforcement learning approach to automatic stroke generation in oriental ink painting. En: IEICE Transactions on Information and Systems E96-D (2013), Nr. 5, p. 1134–1144. – ISBN 9781450312851Xie, Weidi ; Noble, J. A. ; Zisserman, Andrew: Microscopy cell counting and detection with fully convolutional regression networks. En: Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization 6 (2018), may, Nr. 3, p. 283–292. – ISSN 21681171Yang, Wei ; Ouyang, Wanli ; Wang, Xiaolong ; Ren, Jimmy ; Li, Hongsheng ;Wang, Xiaogang: 3D Human Pose Estimation in the Wild by Adversarial Learning. En: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018), p. 5255–5264. – ISBN 9781538664209Yu, Bingqing ; Clark, James J.: WAYLA - Generating Images from Eye Movements. En: 2018 15th Conference on Computer and Robot Vision (CRV), IEEE, may 2018. – ISBN 978–1–5386–6481–0, p. 118–125Zha, Xuewei ; Shi, Fei ; Ma, Yuhui ; Zhu, Weifang ; Chen, Xinjian: Generation of retinal OCT images with diseases based on cGAN. En: Angelini, Elsa D. (Ed.) ; Landman, Bennett A. (Ed.): Medical Imaging 2019: Image Processing, SPIE, mar 2019. – ISBN 9781510625457, p. 74Zhang, Zhifei ; Song, Yang ; Qi, Hairong: Decoupled Learning for Conditional Adversarial Networks. En: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) Vol. 2018-Janua, IEEE, mar 2018. – ISBN 978–1–5386–4886–5, p. 700–708 64 BibliografíaZhao, Bo ; Meng, Lili ; Yin, Weidong ; Sigal, Leonid: Image Generation from Layout. (2018), novZhu, Jun-Yan ; Park, Taesung ; Isola, Phillip ; Efros, Alexei A.: Unpaired Imageto-Image Translation Using Cycle-Consistent Adversarial Networks. En: 2017 IEEE International Conference on Computer Vision (ICCV) Vol. 2017-Octob, IEEE, oct 2017. – ISBN 978–1–5386–1032–9, p. 2242–2251THUMBNAIL1032425183.2020.pdf.jpg1032425183.2020.pdf.jpgGenerated Thumbnailimage/jpeg4501https://repositorio.unal.edu.co/bitstream/unal/79379/4/1032425183.2020.pdf.jpgb17daea2405b403bdca812500739f241MD54ORIGINAL1032425183.2020.pdf1032425183.2020.pdfTesis de Maestría en Ingeniería de Sistemas y Computaciónapplication/pdf29260166https://repositorio.unal.edu.co/bitstream/unal/79379/1/1032425183.2020.pdf224b335b416cc7539a509a845cb999f9MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/79379/2/license.txtcccfe52f796b7c63423298c2d3365fc6MD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81025https://repositorio.unal.edu.co/bitstream/unal/79379/3/license_rdf84a900c9dd4b2a10095a94649e1ce116MD53unal/79379oai:repositorio.unal.edu.co:unal/793792023-08-09 23:04:28.243Repositorio 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