How are GANs Trained?
Generative Adversarial Networks (GANs) are trained using a unique approach involving two neural networks: the Generator and the Discriminator.
1. The Generator:
The Generator is responsible for creating synthetic data. Initially, it generates random noise, which is transformed into data resembling the training set. Its goal is to produce data that is indistinguishable from real data.
2. The Discriminator:
The Discriminator's role is to distinguish between real data and the fake data produced by the Generator. It takes as input both real samples from the training dataset and the generated samples from the Generator.
3. Adversarial Process:
The training process is adversarial. The Generator and Discriminator are trained simultaneously, with the Generator trying to create more convincing data to fool the Discriminator, while the Discriminator improves its ability to identify fake data. This leads to a zero-sum game where the success of one network comes at the expense of the other.
4. Training Loop:
- Generate fake data with the Generator.
- Train the Discriminator on real and fake data, adjusting its weights based on classification accuracy.
- Train the Generator based on the Discriminator's feedback, trying to reduce the difference between real and generated data.
This iterative process continues until the Generator produces data that the Discriminator can no longer reliably differentiate from real data, achieving a balance that signifies successful training.