Hi Pythonistas!,
So far, we’ve seen models that learn from data. Autoencoders learned by: Compressing inputs Reconstructing them
Now we meet a very different idea. What if learning happens through competition?
That’s exactly what Generative Adversarial Networks (GANs) do.
The Core Idea Behind GANs
A GAN has two neural networks:
Generator
Tries to create fake data
Its goal: look real
Discriminator
Looks at data
Decides: real or fake
Both networks train together. One creates. The other judges.
How GAN Training Works
The process goes like this:
- Generator produces fake samples
- Discriminator compares them with real data
- Discriminator gives feedback
- Generator improves using that feedback
- This loop repeats many times.
As the discriminator improves, the generator must improve too.
Learning happens because neither network wants to lose.
Why This Works
The generator never sees rules like:
This is how a face should look Instead, it learns by: Constantly trying to fool the discriminator
The discriminator acts like a moving target. This pressure forces the generator to learn realistic patterns.
What GANs Are Good At
GANs are especially powerful for generation.
They are used for:
- Image generation
- Face synthesis
- Super-resolution (low → high quality images)
- Style transfer
- Creating synthetic data
- Many realistic AI-generated images come from GANs.
The Hard Part of GANs
GANs are powerful, but difficult to train.
Common problems:
Discriminator becomes too strong, Generator collapses and produces similar outputsTraining becomes unstableGAN training requires careful balance. When it works, results are impressive. When it doesn’t, nothing learns.
What I Learned This Week
- GANs use two competing networks
- Generator creates fake data
- Discriminator detects fake data
- Competition drives learning
- Powerful for realistic data generation
What's Coming Next
Next week we will learn about GNNs