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Academic Year 2021/2022
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People's Republic of Algeria Ministry of
Higher
Education
and Scientific Research
Universite Hassiba Benbouali de
Chlef
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Faculty: Exact Sciences and Computer
Science
Department: Computer Science
THESIS FOR OBTAINING BACHELOR'S DEGREE IN
COMPUTER
SCIENCE
presented by :
Chaki Ilyas
Souna Abdelazize
Image to Image Translation with Generative
Adversarial Networks. (Translation of
Satellite Images to Google Maps Images.)
Defended on 12/06/2022 before the jury:
Mr. Ahmed Abbache Supervisor
President Examiner
Contents
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introduction
1 Artificial Intelligence
1.1 Machine Learning
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vii
1
1
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1.1.1 What is learning
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1
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1.1.2 Categories of learning
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2
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1.1.3 Limitations
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2
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1.1.4 Deep Learning
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2
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1.1.4.1 Deep Neural Concepts
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4
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1.1.4.2 Deep neural networks
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4
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1.1.4.3 Error functions
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5
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1.1.4.4 Optimization algorithms:
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6
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1.1.4.5 Deep Neural Network Variants
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7
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1.2
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Natural Language Processing
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9
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1.2.1 What is Language?
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9
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1.2.2 Why Natural Language Processing?
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9
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1.2.2.1 Communication
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10
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1.3
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Computer Vision
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10
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1.3.1 what is computer vision
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10
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1.3.2 Convolution neural network(CNN)
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10
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1.3.2.1 CNN's concepts .
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12
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1.4
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Knowledge Representation
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12
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1.5
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automated reasoning
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13
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1.6
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robotics
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13
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1.6.1 Aspects of robotics .
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13
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1.7
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Conclusion
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13
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2
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Generative Modeling
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14
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2.1
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Representation Learning
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14
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2.1.0.1 Supervised Representational Learning
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14
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2.1.0.2 Unsupervised Representational Learning
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15
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ii
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CONTENTS
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2.2
2.3
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What is generative Modelling
2.2.1 Generative Models
2.2.1.1 AutoEncoders
2.2.1.2 Variational AutoEncoders
Generative Adversarial Networks
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15
16
16
17
18
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2.3.1 What are Generative Adversarial Networks
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18
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2.3.2 Generative Adversarial Network model
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18
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2.3.2.1 The Generator Model
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19
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2.3.2.2 The Discriminator Model
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19
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2.3.3 Generative Adversarial Network Architectures
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19
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3
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The Pix2Pix Model
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21
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3.1
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Image to Image Translation
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21
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3.1.1 Pix2pix model
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22
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3.2
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The U-net Model
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23
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3.2.1 The Unet-Generator Model
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23
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3.2.2 The Markovian Discriminator
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24
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3.2.3 The Model Loss Function
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24
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3.3
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Conclusion
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25
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4
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Project Implementation
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26
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4.1
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Tooling
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26
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4.2
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Conception
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28
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4.3
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The Maps Dataset
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28
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4.4
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Generator Implementation
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29
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4.5
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Discriminator Implementation
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30
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4.6
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Pix2Pix Implementation
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31
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4.7
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Model Training
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31
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4.8
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Model Evaluation
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32
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4.9
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Conclusion
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32
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Listings
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4.1
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encoder_block
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29
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4.2
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decoder_block
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29
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4.3
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generator
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29
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4.4
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encoder_block
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30
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4.5
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decoder_block
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31
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4.6
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decoder_block
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31
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List of Figures
1.1 neuron / Source[26] 3
1.2 artificial neuron / Source[18] 3
1.3 artificial neural network / Source[2] 3
1.4 feed forward neural network / Source[14] 7
1.5 radial basis neural network [20] 8
1.6 modular neural network / Source[19] 8
1.7 recurrent neural network / Source [3] 9
1.8 computer vision architecture / Source[7] 10
1.9 convolution operation / Source [15] 11
2.1 generative modeling in the landscape of artificial
intelligence / Source[1] 15
2.2 AutoEncoder architecture / Source[21] 16
2.3 variational distribution / Source[22] 17
2.4 fake faces generated using cycle GAN / Source [25] 18
2.5 basic GAN architecture / Source[1] 19
2.6 conditional gan architecture / Source [1] 20
3.1 style transfer / Source [25] 22
3.2 unet / Source[1] 23
3.3 Markovian discriminator / Source [1] 24
4.1 python logo / Source[27] 27
4.2 keras logo / Source[11] 27
4.3 Tensor Flow logo / Source[4] 28
4.4 Tkinter symbol /Source [5] 28
4.5 Examples from the data set 29
List of Tables
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1.1
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Types of learning
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2
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1.2
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Deep Learning Concepts
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4
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1.3
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Activation Functions
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5
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1.4
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error Functions
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6
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1.5
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optimization algorithms
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7
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1.6
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Deep Learning Concepts
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12
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acknowledgement
We would love to take this part to show appreciation for everyone
who helped directly or indirectly in making this project. First i will start
with Vincent Kasozi who first inspired us.
i would love to thank him for being our guide and a good
friend
Thanks to Mr.Ahmed Abbache for the opportunity he gave us and for
being extremely patient with the process.
Thanks to Souna Abdelazize for accepting the risky offer of
taking this project even when we knew so little about the domain. Thanks to the
author/creator of every resource that i've used ,the contributions
you'll make is appreciated
I'm trying to make this list as short as possible so i will give
a quick acknowledgement to Douba abdrezzak,Otmani Sadiq,Oueldja Mohammed amine
for helping with the web part.
introduction
The desire to create is one of the deepest yearnings of the human
soul.
Dleter F.Uchtdorf
Mapping technology is one one of the most used technologies in
the last couple years ,with the ever growing need for localisation and
navigation, there are still weak mapping in certain parts of the world this may
require creating efficient maps for various future applications, this will
require new efficient and fast way of generating good maps ,the manual process
of collecting data and using it for making maps can be costful,Google Maps
works with 1,000 third party sources from around the world to collect the data
necessary to create accurate maps. in this project we will introduce a solution
for automating this process using an advanced deep learning architecture.
Artificial intelligence is a
vast field regrouping the humanity quest for replicating and
surpassing our intelligence, ever since the first imagination of artificial
intelligent entities we came a long way. one of the most impressing qualities
of humans is our creativity , this led to a desperate search for a way of
creating efficient generative models . In this project we will go through the
implementation of a Satellite to map
image translator this style transfer is considered as a
generative task ,this will lead to using Generative adversarial networks(GANs),
in the first chapter we will introduce the domain of artificial intelligence
,and talk briefly about machine learning and deep learning ,next in the second
chapter we will talk about generative modeling as leading to generative
adversarial networks and a particular GAN named pix2pix will be used ,in the
last chapter we will go through the code implementation.
1
Artificial Intelligence
Introduction
h Machine Learning
h Natural Language Processing h Computer
Vision
h Knowledge Representation h Automated Reasoning
h Robotics
The Turing test, proposed by Alan Turing (1950), was designed
as a thought experiment that would sidestep the philosophical vagueness of the
question «Can a machine think?» A computer passes the test if a human
interrogator, after posing some written questions, cannot tell whether the
written responses come from a person or from a computer.
1.1 Machine Learning
For the last two decades Machine Learning became one of main
fields in computer science due to the ever growing computing power ,and the
availability of more and more data ,the need to a smart way of data
analysis.
Definition 1.1
the development of computer systems able to learn using
algorithms and statistical models to analyze and draw inferences in data.
[23]
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