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

especialización
Inteligencia de Datos
orientada a Big Data
Esp. Laura Randa Calcagni
e-mail
l.calcagni@gmail.com
Advisor
Dr. Franco Ronchetti
Thesis defense date
July 17, 2020
Generative Adversarial
Networks and applications
SEDICI
http://sedici.unlp.edu.ar/handle/10915/101507
Keywords: GANs ; SRGANs ; Redes Generativas Antagónicas ; Machine Learning
Motivation
There is no doubt about the great importance in the
area of Artificial Intelligence that Generative Adversarial
Networks (GANs) have and will have. The GANs are
a very powerful type of generative model and the most
researched at present.
Since they emerged in 2014, they have been widely
studied due to their enormous potential for applications.
Every week a considerable number of scientific articles
related to this topic are published, in which already
published architectures are implemented in different
solutions 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 very
relevant problem in the field of computer vision. The
generation of high-resolution images from their lowresolution
counterpart has applications in the scientific
field, in diagnostic imaging and in security systems,
among many others.
The general goal of this project is the study of Generative
Adversarial Networks, the construction and design of
examples that allow the understanding and analysis
of each of the parts that constitute them and the
discussion of their possible applications, with greater
emphasis on those applications that have to do with
increasing image resolution.
In a synthetic way, GANs consist of two models (in
general, convolutional neural networks), the generator
and the discriminator, trained simultaneously to
challenge each other, which explains the adversarial
term chosen by the authors to give identity to this
novel method.
On the one hand, the generator is trained to generate
‘fake’ data as close as possible to the real examples of
a 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 corresponding
to the training set (the real examples).
Successively, the two models continually try to beat each
other: the better the generator is at creating convincing
data, the better the discriminator must be at distinguishing
real examples from ‘fake’ ones. This particularity makes
GANs a highly effective generative method.
Thesis Final Work contrinutions
In this project, Adversarial Generative Neural Networks
(GANs) and their applications are studied through an
extensive review of theoretical literature and the
latest scientific papers published.
Generative statistical models are described in
opposition to discriminatory models. The different
blocks that componse a GAN are studied, as well as
their training and the difficulties it involves.
This project also contemplates the implementation
of a Super-Resolution Adversarial Generative Network
(SRGAN) presented in [1], using the libraries Keras and
Tensorflow. The Google Colaboratory environment is
used for training the GAN, since the platform allows the
use of a GPU NVIDIA Tesla K80. In this implementation,
qualitatively high quality images are obtained, which
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