Image to image translation with generative adversiale networks (translation of satelite image to Google maps image )

• Introduction
  
  • 1.1 Machine Learning
    
  • 1.1.1 What is learning
    
  • 1.1 Machine Learning
    
  • 1.1.2 Categories of learning
    
  • 1.1.3 Limitations
    
  • 1.1.4 Deep Learning
    
  • 1.1 Machine Learning
    
  • 1.1.4.1 Deep Neural Concepts
    
  • 1.1.4.2 Deep neural networks
    
  • 1.1.4.3 Error functions
    
  • 1.1.4.4 Optimization algorithms:
    
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  • 1.1 Machine Learning
    
  • 1.1.4.5 Deep Neural Network Variants
    
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  • 1.1 Machine Learning
    
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  • 1.2 Natural Language Processing
    
  • 1.2 Natural Language Processing
    
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  • 1.3 Computer Vision
    
  • 1.2.2.1 Communication
    
  • 1.3 Computer Vision
    
  • 1.3.1 what is computer vision
    
  • 1.3.2 Convolution neural network(CNN)
    
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  • 1.3 Computer Vision
    
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  • 1.4 Knowledge Representation
    
  • 1.3.2.1 CNN's concepts:
    
  • 1.4 Knowledge Representation
    
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  • 1.5 automated reasoning
    
  • 1.5 automated reasoning
    
  • 1.6 robotics
    
  • 1.6.1 Aspects of robotics:
    
  • 1.7 Conclusion
    
• Introduction
  
  • 2.1 Representation Learning
    
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  • 2.1.0.1 Supervised Representational Learning
    
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  • 2.2 What is generative Modelling
    
  • 2.1.0.2 Unsupervised Representational Learning
    
  • 2.2 What is generative Modelling
    
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  • 2.2 What is generative Modelling
    
  • 2.2.1 Generative Models
    
  • 2.2.1.1 AutoEncoders
    
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  • 2.3 Generative Adversarial Networks
    
  • 2.2.1.2 Variational AutoEncoders
    
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  • 2.3 Generative Adversarial Networks
    
  • 2.3 Generative Adversarial Networks
    
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  • 2.3 Generative Adversarial Networks
    
  • 2.3.2.1 The Generator Model
    
  • 2.3.2.2 The Discriminator Model
    
  • 2.3.3 Generative Adversarial Network Architectures
    
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  • 2.3 Generative Adversarial Networks
    
• Introduction
  
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  • 3.1 Image to Image Translation
    
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  • 3.2 The U-net Model
    
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  • 3.2 The U-net Model
    
  • 3.2 The U-net Model
    
  • 3.2.1 The Unet-Generator Model
    
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  • 3.2 The U-net Model
    
  • 3.2.2 The Markovian Discriminator
    
  • 3.2.3 The Model Loss Function
    
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  • 3.3 Conclusion
    
  • 3.3 Conclusion
    
  • 4.1 Tooling
    
  • 4.2 Conception
    
  • 4.2 Conception
    
  • 4.3 The Maps Dataset
    
  • 4.4 Generator Implementation
    
  • 4.5 Discriminator Implementation
    
  • 4.5 Discriminator Implementation
    
  • 4.6 Pix2Pix Implementation
    
  • 4.7 Model Training
    
  • 4.8 Model Evaluation
    
  • 4.8 Model Evaluation
    
  • 4.9 Conclusion