Vision Specialists: CNNs, GANs, and Diffusion
The architectures built for visual data. Understanding when to analyze images versus generate them.
Three Tools for Visual AI
When working with images and video, three architectures dominate: CNNs for analysis, GANs for generation, and Diffusion Models as the newer generation approach. Understanding their differences is crucial for choosing the right tool.
CNNs: "What's in this image?" (Analysis)
GANs: "Create an image like this" (Generation via competition)
Diffusion: "Refine noise into an image" (Generation via denoising)
CNNs: Understanding Images
Convolutional Neural Networks are designed to analyze and understand visual data. They use convolutional layers—filters that scan across images detecting patterns like edges, shapes, and textures.
Layer 1: Detects edges and simple patterns
Layer 2: Combines edges into shapes
Layer 3: Recognizes parts (eyes, wheels, corners)
Layer 4+: Identifies objects and scenes
Each layer builds on the previous, creating hierarchical understanding.
| CNN Use Case | Example | Industry |
|---|---|---|
| Image classification | Cat vs dog, tumor detection | Healthcare, consumer |
| Object detection | Find all cars in photo | Autonomous vehicles |
| Facial recognition | Unlock phone, security | Security, consumer |
| Document analysis | OCR, form processing | Finance, legal |
| Quality inspection | Detect manufacturing defects | Manufacturing |
GANs: Creating Images
Generative Adversarial Networks take a completely different approach. Instead of analyzing images, they generate new ones from scratch using two competing networks.
Generator: Creates fake images from random noise
Discriminator: Tries to distinguish real from fake
They train together in competition. Generator improves at creating convincing fakes. Discriminator improves at catching them. Eventually, generator wins—producing images indistinguishable from real ones.
| GAN Use Case | Example | Notable Project |
|---|---|---|
| Face generation | Synthetic portraits | ThisPersonDoesNotExist |
| Style transfer | Photo to painting | Prisma app |
| Image enhancement | Upscaling, restoration | NVIDIA DLSS |
| Deepfakes | Face swapping | Various (controversial) |
| Synthetic data | Training data generation | Enterprise AI |
Diffusion Models: The New Standard
Diffusion Models are the newest approach, now powering DALL-E 3, Midjourney, and Stable Diffusion.
Training: Learn to reverse noise—given a noisy image, predict the less noisy version
Generation: Start with pure noise, iteratively denoise until coherent image emerges
Think of it like sculpting: start with a rough block (noise) and gradually refine it into a detailed sculpture (image).
Stability: Much easier to train than GANs (no mode collapse)
Quality: Produces higher quality, more diverse outputs
Control: Better at following text prompts
Tools: DALL-E 3, Midjourney, Stable Diffusion all use diffusion
Quick Decision Guide
| Task | Best Architecture | Why |
|---|---|---|
| Analyze images | CNN | Designed for understanding |
| Generate images | Diffusion | Higher quality, more stable |
| Generate video | GAN | Still leads for motion |
| Synthetic training data | GAN or specialized tools | Fast, controllable |
| Real-time enhancement | GAN | Faster inference |