Machine Learning Made Simple

Ep29: Exploring GANs: From CoGAN to StyleGAN

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Description:

Join us on this deep dive into the fascinating world of Generative Adversarial Networks (GANs). In this episode, we explore the key advancements in GAN technology and their impact on the AI landscape.

Episode Highlights:

  • CoGAN: Understanding Conditional Generative Adversarial Nets and their applications.
  • DCGAN: Unsupervised representation learning with Deep Convolutional GANs.
  • pix2pix: Innovations in image-to-image translation with Conditional Adversarial Networks.
  • WGAN: Insights into Wasserstein GAN and its improvements over traditional GANs.
  • CycleGAN: Exploring unpaired image-to-image translation using cycle-consistent adversarial networks.
  • ProGAN: Delving into the progressive growing of GANs for enhanced quality, stability, and variation.
  • StyleGAN: A comprehensive look at the style-based generator architecture for GANs.

    • Tune in to gain valuable insights into these groundbreaking technologies and their real-world applications.


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      10. References for main topic:

      1. CoGAN -  [1411.1784] Conditional Generative Adversarial Nets

      2. DCGAN [1511.06434] Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks 

      3. pix2pix [1611.07004] Image-to-Image Translation with Conditional Adversarial Networks 

      4. WGAN [1701.07875] Wasserstein GAN 

      5. CycleGAN  [1703.10593] Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks 

      6. ProGAN [1710.10196] Progressive Growing of GANs for Improved Quality, Stability, and Variation 

      7. StyleGAN  [1812.04948] A Style-Based Generator Architecture for Generative Adversarial Networks 

        8. ...more
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        Machine Learning Made SimpleBy Saugata Chatterjee