Overview of GAN Structure

A generative adversarial network (GAN) has two parts:

• The generator learns to generate plausible data. The generated instances become negative training examples for the discriminator.
• The discriminator learns to distinguish the generator's fake data from real data. The discriminator penalizes the generator for producing implausible results.

Training process of GAN

• When training begins, the generator produces obviously fake data, and the discriminator quickly learns to tell that it's fake.
• As training progresses, the generator gets closer to producing output that can fool the discriminator.
• Finally, if generator training goes well, the discriminator gets worse at telling the difference between real and fake. It starts to classify fake data as real, and its accuracy decreases.

Here's a picture of the whole system:

• Both the generator and the discriminator are neural networks. The generator output is connected directly to the discriminator input. Through backpropagation, the discriminator's classification provides a signal that the generator uses to update its weights.

Model optimizing using training

The animation below visualizes the images generated by the GAN during training for a particular random sample.