Generative Adversarial Networks
A Generative Adversarial Network (GAN) trains two neural networks in opposition: a Generator (G) that maps latent noise (optionally conditioned on input) into synthetic samples, and a Discriminator (D) that distinguishes real data from generated data and provides gradients that drive G to improve; training is a minimax game where D maximizes correct real/fake classification while G minimizes D’s ability to detect fakes, producing a model that can sample from a learned data distribution without explicit likelihood modeling.
This repo contains notebook implementations of multiple GAN families and a production-grade system design that describes how to operationalize generative models end-to-end (versioned data → training → evaluation gates → deployment → monitoring → retraining).
GAN Architecture Progression + Production Design
This repo is a GAN architecture progression implemented as notebooks plus a production-grade system design for training/serving/monitoring generative models. The production layer is a design spec, not an infra codebase (no Kubernetes manifests, CI pipelines, or serving service implementation).
What is a GAN
A GAN is a two-network minimax game:
-
Generator (G): maps input (noise or condition) → synthetic sample. learns a mapping from latent/condition input to a synthetic sample.
G: (z, c) → x̂ -
Discriminator (D): predicts real vs fake and supplies gradients to G learns a binary classifier over samples and supplies gradients to G.
D: x → [0, 1]
Objective
min_G max_D E_{x~p_data}[log D(x)] + E_{z~p(z)}[log(1 − D(G(z)))]
Training loop
- Discriminator step (maximize): increase
D(x)for real data and decreaseD(G(z))for generated samples. - Generator step (minimize): update G to increase
D(G(z))while D is frozen.
Implication
GAN training is not likelihood-based. It is a coupled game. Loss curves are weak quality signals; use sample grids across checkpoints and multi-seed diversity as primary diagnostics.
Project Summary
| # | Project | Architecture | Capability demonstrated | Input → Output |
|---|---|---|---|---|
| 1 | Slanted Land | Vanilla GAN | adversarial dynamics in the smallest setting | noise → 2×2 binary image |
| 2 | Fake Faces | DCGAN | convolutional synthesis + improved stability | noise → face image |
| 3 | Frontalization | Pix2Pix (cGAN) | paired conditional translation | angled face → frontal face |
| 4 | Text-to-Image | StackGAN Stage-I/II | text conditioning + staged refinement | caption embedding → image |
| 5 | High-Res Synthesis | StyleGAN2 + ADA | high-res realism + small-data stabilization | latent → high-res image |
Key design decisions
Vanilla GAN (2×2)
Focus: isolate the adversarial loop without convolutional complexity.
- shallow MLP G/D
- tiny binary image domain to visualize collapse and instability quickly
DCGAN
Focus: add convolutional inductive bias to stabilize image synthesis.
- conv G/D
- BatchNorm + ReLU / LeakyReLU
- latent sampling + grid visualization as primary eval
Pix2Pix (cGAN)
Focus: enforce conditional consistency using paired supervision.
- G conditioned on source image
- D judges realism + (source, output) consistency
- inspect alignment artifacts (edges, symmetry, texture continuity)
StackGAN Stage-I/II
Focus: split the problem into layout then refinement.
- Stage-I: coarse structure from text embedding
- Stage-II: refine into higher fidelity
- primary risk: text-image mismatch; inspect adherence vs realism trade-off
StyleGAN2 + ADA
Focus: photorealistic high-res synthesis with small-data stability.
- style-based generator (controllable latent transformations)
- ADA adjusts augmentation strength to reduce discriminator overfit
- showcase: style mixing and latent interpolation panels
System design
The design doc frames GAN work as a full ML lifecycle:
- versioned data + artifacts
- training + validation gates
- orchestration + CI/CD
- GPU serving + autoscaling
- monitoring + drift triggers
- security (IAM/VPC/KMS/secrets)