Image Generation with AI: Image Generation Frameworks





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.




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Image Generation with AI: Image Generation Frameworks

  • 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


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