Generative Models
GANs Deep Dive — Mastering Generative Adversarial Networks
GANs revolutionized generative modeling by pitting two neural networks against each other in a minimax game. The generator creates realistic samples while the discriminator tries to tell real from fake, driving both to improve until generated data is indistinguishable from reality.
- Key point 1 — Minimax game converges when generator matches data distribution (Nash equilibrium)
- Key point 2 — DCGAN, WGAN, and StyleGAN each solve different training challenges
- Key point 3 — FID score is the standard metric for evaluating generation quality
"In the battle between generator and discriminator, everyone wins."
GANs Deep Dive — Generative Adversarial Networks
GANs learn to generate realistic data by pitting two neural networks against each other in a minimax game: a generator creates fake samples, and a discriminator tries to distinguish real from fake.
The GAN Framework
How this diagram works: This diagram illustrates the adversarial game at the heart of GANs. The generator (green, left) takes random noise and produces fake data , attempting to mimic real data. The discriminator (red, right) receives both real data and fake samples , and outputs a probability indicating whether each input is real or generated. The dashed feedback arrows show the competing objectives: the generator loss pushes to fool (making ), while the discriminator loss pushes to correctly classify both real and fake inputs. Training reaches Nash equilibrium when the generator perfectly matches the real data distribution () and the discriminator can no longer tell them apart, outputting for all inputs.
Nash Equilibrium
Training Challenges
DCGAN (Deep Convolutional GAN)
WGAN (Wasserstein GAN)
StyleGAN
PyTorch Implementation
Training Tips
Practice Exercises
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Train DCGAN on CIFAR-10: Generate realistic images. Monitor FID over training.
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WGAN-GP implementation: Replace BCE loss with Wasserstein loss + gradient penalty. Compare training stability.
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Mode collapse experiment: Train a GAN on MNIST and observe mode collapse. Fix it with minibatch discrimination.
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Style mixing: Implement StyleGAN and experiment with style mixing at different layers.
Key Takeaways
What to Learn Next
-> Variational Autoencoders Learn probabilistic latent representations with encoder-decoder architectures.
-> Diffusion Models Deep Dive Explore the math behind gradual noising and denoising for image generation.
-> Self-Supervised Learning Learn useful representations from unlabeled data without manual annotation.
-> DL Systems Design Master distributed training, monitoring, and production deployment of deep learning models.
-> Model Compression Make deep learning models fast and efficient for production deployment.
-> Graph Neural Networks Learn from graph-structured data with message passing and attention mechanisms.