Generative Adversarial Networks (GANs) are a class of deep learning algorithms that are revolutionizing the field of machine learning. GANs are a type of unsupervised learning algorithm, which means they do not require labeled training data. Instead, GANs use two networks, a generative network and a discriminative network, that compete with each other.
The generative network is responsible for generating new data samples, while the discriminative network is responsible for determining whether a sample is real or generated. The two networks are trained simultaneously, with the generative network attempting to fool the discriminative network into believing its generated samples are real, while the discriminative network attempts to identify the generated samples.
GANs have been used for a variety of tasks, including image synthesis, image-to-image translation, text-to-image generation, and more. In image synthesis, GANs can generate realistic looking images from a given set of input images. In image-to-image translation, GANs can be used to convert images from one domain to another. For example, GANs can be used to convert images of horses to zebras. In text-to-image generation, GANs can be used to generate images from a given text description.
GANs have been shown to be a powerful tool for many machine learning tasks, and are continuing to be improved upon. They are a great example of how unsupervised learning algorithms can be used to solve complex problems.