Generative Adversarial Networks (GANs) are a class of artificial intelligence models used for generating data, typically images, that resemble some input data distribution. GANs were introduced by Ian Goodfellow and his colleagues in 2014 and have since become one of the most popular and influential frameworks in the field of machine learning and generative modeling.
The fundamental idea behind GANs is to set up a two-player game between two neural networks: a generator and a discriminator. These networks are trained simultaneously through a process of competition and collaboration, which leads to the generation of increasingly realistic data samples.
Let’s dive into the working of GANs step by step:
Generator Network (G):
The generator’s role is to create synthetic data samples that resemble real data. It takes random noise as input and tries to produce data that appears as though it could come from the real data distribution. In the context of image generation, the generator typically starts with random noise and learns to create images that resemble real images.
Discriminator Network (D):
The discriminator’s role is to distinguish between real data and synthetic data generated by the generator. It takes both real and generated data as input and outputs a probability score indicating how likely the input data is to be real. Initially, the discriminator’s performance might not be very good, but as training progresses, it becomes better at distinguishing real from fake data.
The training of GANs happens in alternating steps:
Training the Discriminator: In this step, the discriminator is trained using a dataset of real data and a dataset of fake data generated by the generator. The discriminator learns to assign high probabilities to real data and low probabilities to fake data.
Training the Generator: The generator is trained to produce data that fools the discriminator. It generates synthetic data samples and sends them through the discriminator. The generator’s objective is to generate data that the discriminator misclassifies as real. The gradients of the misclassified samples’ scores are back propagated through the generator to improve its ability to generate more convincing data.
These two steps are repeated iteratively, with the generator and discriminator improving their performance through this adversarial process. Over time, the generator becomes better at creating data that is increasingly difficult for the discriminator to distinguish from real data. Ideally, this results in the generator producing highly realistic data samples.
The training process continues until a certain level of performance is achieved or a predefined number of iterations is reached. It’s important to note that training GANs can be challenging. The balance between the generator and discriminator training is delicate, and mode collapse (where the generator produces limited varieties of data) and other issues can arise.
In summary
GANs work by training a generator and a discriminator in a competitive manner, where the generator aims to create data that is indistinguishable from real data, while the discriminator aims to accurately classify real and fake data. This process drives the generator to produce increasingly realistic data samples.
