Generative Adversarial Networks Specialization

What you will learn from this course

  • Learn about GANs and their applications
  • Understand the intuition behind the fundamental components of GANs
  • Explore and implement multiple GAN architectures
  • Build conditional GANs capable of generating examples from determined categories
  • Assess the challenges of evaluating GANs and compare different generative models
  • Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs
  • Identify sources of bias and the ways to detect it in GANs
  • Learn and implement the techniques associated with the state-of-the-art StyleGANs
  • Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity
  • Leverage the image-to-image translation framework and identify applications to modalities beyond images
  • Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa)
  • Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures
  • Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one
  • Understand GAN components, build basic GANs using PyTorch and advanced DCGANs using convolutional layers, control your GAN and build conditional GAN
  • Compare generative models, use FID method to assess GAN fidelity and diversity, learn to detect bias in GAN, and implement StyleGAN techniques
  • Use GANs for data augmentation and privacy preservation, survey GANs applications, and examine and build Pix2Pix and CycleGAN for image translation
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