Image Generation Frameworks

Generative AI    |    Intermediate
  • 17 videos | 1h 55m
  • Includes Assessment
  • Earns a Badge
Amazing, controversial, and game-changing. It's remarkable to think that we're only at the beginning, only starting to see the opportunities offered by artificial intelligence (AI)-powered image generation. Yet here we are, at the start of a technological marvel that's taking the world by storm. In this course, you'll explore image generation frameworks, beginning with variational autoencoders (VAEs), generative adversarial networks (GANs), and comparing GANs and VAEs. Then you'll explore GAN architectures, GAN use cases, GAN training, and the DCGAN, WGAN, CycleGAN, and StyleGAN architectures. Finally, you'll learn about autoregressive models, autoregressive models in comparison to other techniques, diffusion models, diffusion model use cases, and the pros and cons of image generation frameworks.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Provide an overview of the foundational concepts of variational autoencoders (vaes) and their role in image generation
    Outline the principles behind generative adversarial networks (gans) and their significance in artificial intelligence (ai)-generated visuals
    Outline the differences between gans and vaes in terms of generative capabilities
    Provide an overview of various gan architectures, including dcgan, wgan, cyclegan, and stylegan
    Outline real-world applications of gans in fields such as art and fashion
    Outline the training process of a basic gan
    Provide an overview of the dcgan architecture and how it's implemented
    Provide an overview of the wgan architecture and how it's implemented
  • Provide an overview of the cyclegan architecture and how it's implemented
    Provide an overview of the stylegan architecture and how it's implemented
    Outline autoregressive models and their pixel-level image generation approach
    Compare autoregressive models to other image generation techniques
    Outline the concept of diffusion models and their innovative approach to image generation
    Outline practical use cases of diffusion models in generating diverse visual content
    Recognize the advantages and limitations of various image generation frameworks
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 9s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 9m 30s
    After completing this video, you will be able to provide an overview of the foundational concepts of variational autoencoders (VAEs) and their role in image generation. FREE ACCESS
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    3.  Generative Adversarial Networks (GANs)
    8m 51s
    Upon completion of this video, you will be able to outline the principles behind generative adversarial networks (GANs) and their significance in artificial intelligence (AI)-generated visuals. FREE ACCESS
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    4.  Comparing GANs and VAEs
    7m 54s
    After completing this video, you will be able to outline the differences between GANs and VAEs in terms of generative capabilities. FREE ACCESS
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    5.  Introduction to GAN Architectures
    7m 32s
    Upon completion of this video, you will be able to provide an overview of various GAN architectures, including DCGAN, WGAN, CycleGAN, and StyleGAN. FREE ACCESS
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    6.  GAN Use Cases
    7m 15s
    After completing this video, you will be able to outline real-world applications of GANs in fields such as art and fashion. FREE ACCESS
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    7.  GAN Training
    6m 23s
    Upon completion of this video, you will be able to outline the training process of a basic GAN. FREE ACCESS
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    8.  Implementing the DCGAN Architecture
    8m 32s
    In this video, we will provide an overview of the DCGAN architecture and how it's implemented. FREE ACCESS
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    9.  Implementing the WGAN Architecture
    6m 20s
    In this video, we will provide an overview of the WGAN architecture and how it's implemented. FREE ACCESS
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    10.  Implementing the CycleGAN Architecture
    7m 52s
    In this video, we will provide an overview of the CycleGAN architecture and how it's implemented. FREE ACCESS
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    11.  Implementing the StyleGAN Architecture
    7m 10s
    In this video, we will provide an overview of the StyleGAN architecture and how it's implemented. FREE ACCESS
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    12.  Autoregressive Models
    9m 45s
    After completing this video, you will be able to outline autoregressive models and their pixel-level image generation approach. FREE ACCESS
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    13.  Comparing Autoregressive Models to Other Techniques
    7m 13s
    Upon completion of this video, you will be able to compare autoregressive models to other image generation techniques. FREE ACCESS
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    14.  Diffusion Models
    5m 52s
    After completing this video, you will be able to outline the concept of diffusion models and their innovative approach to image generation. FREE ACCESS
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    15.  Diffusion Model Use Cases
    6m 29s
    Upon completion of this video, you will be able to outline practical use cases of diffusion models in generating diverse visual content. FREE ACCESS
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    16.  Pros and Cons of Image Generation Frameworks
    6m 31s
    After completing this video, you will be able to recognize the advantages and limitations of various image generation frameworks. FREE ACCESS
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    17.  Course Summary
    44s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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