10-Week Syllabus

Week 1 : How to Make a Dog From Noise

Introduction to Generative Models & Building Your First GAN
  • Introduction to Generative Models vs. Discriminative Models, and where GANs are situated in this context
  • Intuition Behind GANs
    • Role of the discriminator
    • Role of the generator
    • BCE loss
    • Training vs. Inference
  • Deep Convolutional GANs
  • Review of Pytorch, convolutions, activation functions, batch normalization, padding & striding, pooling & upsampling, transposed convolutions
  • Mode Collapse and Problems with BCE Loss
  • Earth Mover’s Distance (Wasserstein Distance)
  • Wasserstein-Loss
  • Condition on W-loss Critic
  • 1-Lipschitz Continuity Enforcement

Week 2 : Picking a Breed of Dog to Generate

Controllable Generation and Conditional GAN [Problem set 1 due]
  • Conditional Generation: Intuition & Inputs
  • Controllable Generation and how it is situated vis-a-vis Conditional Generation
  • Vector Algebra in Latent Space
  • Challenges with Controllable Generation
  • Using Classifier Gradients for Controllable Generation
  • Supervised disentanglement
  • Evaluation: Inception Score, Frechet Inception Distance, HYPE, classifier-based evaluation of Disentanglement
  • Challenges in Generative Model evaluation, particularly GANs, Importance of Evaluation
  • Fidelity vs. Diversity Tradeoffs, Truncation Trick Sampling
  • Inception Embeddings vs. Pixel Comparisons
  • Inception Score: Intuition, Shortcomings
  • Frechet Inception Distance: Intuition, Shortcomings
  • Gold Standard in Fidelity (human-centered approach)
  • Intuition of Precision vs. Recall in Generative Models
  • Evaluating Disentanglement using the Classifier Method, Perceptual Path Length

Week 3 : Making High Quality Faces (and Other Complex Things)

Advancements in GANs and State of the Art Improvements for StyleGAN, Fine-tuning GANs [Problem set 2 due]
  • Components of StyleGAN:
    • Disentangled Intermediate Latent W-Space
    • Noise Injection at Multiple Layers (Increased Style Supervision)
    • Uncorrelated Noise for Stochasticity
    • Adaptive Instance Normalization
    • Progressive Growing
  • StyleGAN2
  • Bias
  • Fine-tuning Large GANs, Pros/Cons

Week 4 : Changing Painters, Species, and Seasons

Image-to-Image Translation [Problem set 3 due] [Project Inspo Part 1 Due]
  • Pix2Pix for Paired Image-to-Image Translation
    • U-Net, Skip Connections
    • PatchGAN
  • CycleGAN for Unpaired Image-to-Image Translation
    • Cycle Consistency
    • Identity Loss
  • Multimodal Generation:
    • Shared Latent Space Assumption (UNIT)
    • Extended to Multimodal (MUNIT)
  • Beyond Image-to-Image: Other Translation Forms
    • GauGAN: Instance Segmentation to Images
    • Text-to-Image, Image-to-Text
    • Musical-Notes-to-Melody
  • Data augmentation
  • Image Editing, In-painting, and GAN Inversion
  • Image Editing, Photoshop 2.0
  • Inverting a GAN, Challenges from Increasing Model Size, BiGAN
  • GAN Inversion vs. Image Optimization
  • Combining Inversion Techniques (“Warm Start”) with Optimization

Week 5 : Reading (and re-reading)

Reading papers in GANs [Problem set 4 due] [Project Inspo Part 2 Due]
  • Approach to Reading Research Papers: Skim Twice, Read Twice
  • Model Diagrams and Common Representations
  • Adjacent Areas of Research: adversarial learning, robustness/adversarial attacks
  • Compelling applied areas and research:
    • Healthcare
    • Climate change

Week 6: Projects

[Project Proposal Due]
  • Meeting with Mentors for feedback and direction

Week 7: Projects

[Milestone 1 Due]

Week 8: Projects

Meeting with Mentors

Week 9: Projects

[Milestone 2 Due]

10: The Finale

[Final Project due]