Tesis y Tesistas 2020 - Postgrado - Fac. de Informática - UNLP

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especializaciónInteligencia de Datosorientada a Big DataEsp. Laura Randa Calcagnie-maill.calcagni@gmail.comAdvisorDr. Franco RonchettiThesis defense dateJuly 17, 2020Generative AdversarialNetworks and applicationsSEDICIhttp://sedici.unlp.edu.ar/handle/10915/101507Keywords: GANs ; SRGANs ; Redes Generativas Antagónicas ; Machine LearningMotivationThere is no doubt about the great importance in thearea of Artificial Intelligence that Generative AdversarialNetworks (GANs) have and will have. The GANs area very powerful type of generative model and the mostresearched at present.Since they emerged in 2014, they have been widelystudied due to their enormous potential for applications.Every week a considerable number of scientific articlesrelated to this topic are published, in which alreadypublished architectures are implemented in differentsolutions or new ones are proposed for a certain purpose.As such, it is a field of research that is widely growing.Within the wide range of applications allowed by GANs,super-resolution (SR) imaging is an interesting and veryrelevant problem in the field of computer vision. Thegeneration of high-resolution images from their lowresolutioncounterpart has applications in the scientificfield, in diagnostic imaging and in security systems,among many others.The general goal of this project is the study of GenerativeAdversarial Networks, the construction and design ofexamples that allow the understanding and analysisof each of the parts that constitute them and thediscussion of their possible applications, with greateremphasis on those applications that have to do withincreasing image resolution.In a synthetic way, GANs consist of two models (ingeneral, convolutional neural networks), the generatorand the discriminator, trained simultaneously tochallenge each other, which explains the adversarialterm chosen by the authors to give identity to thisnovel method.On the one hand, the generator is trained to generate‘fake’ data as close as possible to the real examples ofa certain training set that is selected. On the other hand,the discriminator is trained to be able to discern the ‘fake’data produced by the generator from those correspondingto the training set (the real examples).Successively, the two models continually try to beat eachother: the better the generator is at creating convincingdata, the better the discriminator must be at distinguishingreal examples from ‘fake’ ones. This particularity makesGANs a highly effective generative method.Thesis Final Work contrinutionsIn this project, Adversarial Generative Neural Networks(GANs) and their applications are studied through anextensive review of theoretical literature and thelatest scientific papers published.Generative statistical models are described inopposition to discriminatory models. The differentblocks that componse a GAN are studied, as well astheir training and the difficulties it involves.This project also contemplates the implementationof a Super-Resolution Adversarial Generative Network(SRGAN) presented in [1], using the libraries Keras andTensorflow. The Google Colaboratory environment isused for training the GAN, since the platform allows theuse of a GPU NVIDIA Tesla K80. In this implementation,qualitatively high quality images are obtained, which132
englishEsp. Laura Randa Calcagnican be appreciated both using as inputimages of thetest set and images unrelated to the data set used totrain the network.¡1. Christian Ledig, Lucas Theis, Ferenc Huszár, JoséAntonio Caballero, Andrew Aitken, Alykhan Tejani,Johannes Totz, Zehan Wang, and Wenzhe Shi. Photorealisticsingle image super-resolution using agenerative adversarial network. 2017 IEEE Conference onComputer Vision and Pattern Recognition (CVPR), pages105–114, 2016.Future Research LinesThe Generative Adversarial Networks have a verywide range of possible applications that are beingimplemented in industry/ research or are currentlybeing developed and studied. Among these possibleapplications, the most successful so far have been inthe area of computer vision and imaging.133 TESIS Y TESISTAS 2020
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índiceEquipo Editorialpag. 4MAESTR
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equipoeditorialDIRECTOR DE POSTGRAD
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01DOCTORADO ENCIENCIAS INFORMÁTICA
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españolDr. Facundo Manuel Quiroga5
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englishDr. Facundo Manuel Quirogaeq
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españolDr. Diego Miguel Montezanti
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englishDr. Diego Miguel Montezantit
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españolDra. Verónica ArtolaAsí,
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englishDr. Verónica Artola• The
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españolDra. Patricia Rosalia Jimbo
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englishDr. Patricia Rosalia Jimbo S
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españolDr. Sergio Hernán Rocabado
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englishDr. Sergio Hernán Rocabado
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españolDra. Noelia Soledad Pintoat
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englishDr. Noelia Soledad Pintoof e
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DOCTORADO ENCIENCIAS INFORMÁTICASD
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españolDr. Nahuel Mangiaruautiliza
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englishDr. Nahuel Mangiaruaand reco
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MAESTRÍATECNOLOGÍA INFORMÁTICAAP
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MAESTRÍAINGENIERÍA DE SOFTWAREMg.
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Page 115 and 116: españolDr. José Manuel Ochoa Robl
Page 117 and 118: englishDr. José Manuel Ochoa Roble
Page 119 and 120: españolEsp. Mario Alberto Vincenzi
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Page 123 and 124: españolEsp. Nicolás Martín Páez
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Page 127 and 128: españolEsp. Cesar Armando Estrebou
Page 129 and 130: englishEsp. Cesar Armando Estrebouw
Page 131: españolEsp. Laura Randa Calcagnius
Page 135 and 136: especializaciónIngeniería de Soft
Page 137 and 138: JURADOSDESIGNADOSLic. Azrilevich Pa
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Tesis y Tesistas 2020 - Postgrado - Fac. de Informática - UNLP

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