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A Generative Adversarial Network (GAN) is a type of artificial intelligence algorithm used in unsupervised machine learning. GANs were introduced by Ian Goodfellow and his colleagues in 2014 and have since become a significant advancement in the field of generative modeling. Here’s a comprehensive overview of GANs:
1. Objective:
- GANs are designed to generate new, realistic data samples that resemble a given dataset. The model consists of two neural networks, a generator, and a discriminator, which are trained simultaneously through adversarial training.
2. Architecture:
Generator:
- The generator takes random noise as input and transforms it into synthetic data samples.
- It starts with random noise and gradually refines its output to resemble real data.
- Typically consists of transposed convolutional layers to upsample the data.
Discriminator:
- The discriminator evaluates whether a given input is real (from the actual dataset) or fake (generated by the generator).
- It is trained to improve its ability to distinguish between real and fake samples.
3. Training Process:
- The generator and discriminator are trained in a competitive manner.
- The generator aims to produce realistic samples that can fool the discriminator, while the discriminator aims to correctly classify real and fake samples.
- The training process is often described as a minimax game, where the generator and discriminator are in constant competition.
4. Loss Function:
- The loss function in GANs is a combination of two components:
- Generator Loss: Measures how well the generator is fooling the discriminator.
- Discriminator Loss: Measures how well the discriminator is distinguishing between real and fake samples.
5. Applications:
- GANs have found applications in various domains, including:
