Perceptron / Neuron

Neural Network

Goal: optimise some loss function (mean square error, cross-entropy...)

Generative Adversarial Network (GAN)

We want to:

• Learn the distribution that generates the data p(x).
• Generate new samples from p(x)

How we do it:

• Two CNNs: Generator G and Discriminator D.
• We train G to generate new "fake" samples to trick D.
• We train D to distinguish fake and real samples.
• Training ends when there is a "stalemate".

Generative Adversarial Network (GAN)

Example