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Apply Generative Adversarial Networks (GANs)
  • Level Professional
  • Duration 27 hours
  • Course by DeepLearning.AI
  • Offered by

About

In this course, you will: - 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 The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.

Modules

GANs for Data Augmentation and Privacy

    • 7 Videos
    • 9 Readings
    • 1 Programming
    • 1 Assignment
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1 Programming

  • Data Augmentation

1 Assignment

  • GANs Hippocratic Oath

7 Videos

  • Welcome to Course 3
  • Welcome to Week 1
  • Overview of GAN Applications
  • Data Augmentation: Methods and Uses
  • Data Augmentation: Pros & Cons
  • GANs for Privacy
  • GANs for Anonymity

9 Readings

  • Syllabus
  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!
  • (Optional) Automated Data Augmentation
  • (Optional) Lecture Notes W1
  • (Optional Notebook) Generative Teaching Networks
  • (Optional) Talking Heads
  • (Optional) De-identification
  • (Optional) GAN Fingerprints
  • Works Cited

Image-to-Image Translation with Pix2Pix

    • 8 Videos
    • 6 Readings
    • 2 Programming
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2 Programming

  • U-Net
  • Pix2Pix

8 Videos

  • Welcome to Week 2
  • Image-to-Image Translation
  • Pix2Pix Overview
  • Pix2Pix: PatchGAN
  • Pix2Pix: U-Net
  • Pix2Pix: Pixel Distance Loss Term
  • Pix2Pix: Putting It All Together
  • Pix2Pix Advancements

6 Readings

  • (Optional) Lecture Notes W2
  • (Optional) The Pix2Pix Paper
  • (Optional Notebook) Pix2PixHD
  • (Optional) More Work Using PatchGAN
  • (Optional Notebook) GauGAN
  • Works Cited

Unpaired Translation with CycleGAN

    • 9 Videos
    • 8 Readings
    • 1 Programming
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1 Programming

  • CycleGAN

9 Videos

  • Welcome to Week 3
  • Unpaired Image-to-Image Translation
  • CycleGAN Overview
  • CycleGAN: Two GANs
  • CycleGAN: Cycle Consistency
  • CycleGAN: Least Squares Loss
  • CycleGAN: Identity Loss
  • CycleGAN: Putting It All Together
  • CycleGAN Applications & Variants

8 Readings

  • (Optional) Lecture Notes W3
  • [IMPORTANT] Reminder about end of access to Lab Notebooks
  • (Optional) The CycleGAN Paper
  • (Optional) CycleGAN for Medical Imaging
  • (Optional Notebook) MUNIT
  • Works Cited
  • Acknowledgements
  • (Optional) Opportunity to Mentor Other Learners

Auto Summary

Dive into the world of Generative Adversarial Networks (GANs) with this comprehensive course designed for Data Science and AI enthusiasts. Led by Coursera, this professional-level program covers practical applications like data augmentation and privacy, alongside hands-on projects with Pix2Pix and CycleGAN using PyTorch. Suitable for all learners, the course spans 1620 minutes and offers flexible subscription options. Perfect for those eager to apply GANs to their projects or build foundational knowledge in AI.

Sharon Zhou
Instructors

Sharon Zhou

Eda Zhou
Instructors

Eda Zhou

Eric Zelikman
Instructors

Eric Zelikman