Generative Adversarial Networks (GANs) are a type of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to learn the probability distribution that generated a set of training examples. GANs are among the most successful generative models, especially in generating realistic high-resolution images. They are based on game theory, with two neural networks, the generator and the discriminator, competing to improve their performance. The generator creates data samples that mimic the training data, while the discriminator tries to distinguish between real and generated samples. The generator aims to fool the discriminator, while the discriminator aims to correctly identify real samples. This adversarial process leads to the generation of realistic data. GANs have been successfully applied to various tasks, but they still present unique challenges and research opportunities. The generator learns to approximate the data distribution without explicitly modeling the density function, making them implicit generative models. The training process involves minimizing the cost function for both the generator and the discriminator, with the generator trying to minimize the negative log-likelihood that the discriminator assigns to the wrong labels. This process is illustrated in Figure 3, where the generator and discriminator are trained simultaneously. The theoretical foundation of GANs includes the concept of Nash equilibrium, where both the generator and the discriminator have no incentive to change their strategies. However, achieving this equilibrium is challenging, and the training process often requires careful tuning. The success of GANs lies in their ability to generate realistic data without requiring explicit density modeling, making them effective for tasks such as image generation, data synthesis, and domain adaptation. Despite their success, GANs still face challenges in convergence and stability, and ongoing research aims to improve their performance and reliability. GANs have shown great promise in various applications, including image generation, data augmentation, and machine learning tasks where labeled data is scarce.Generative Adversarial Networks (GANs) are a type of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to learn the probability distribution that generated a set of training examples. GANs are among the most successful generative models, especially in generating realistic high-resolution images. They are based on game theory, with two neural networks, the generator and the discriminator, competing to improve their performance. The generator creates data samples that mimic the training data, while the discriminator tries to distinguish between real and generated samples. The generator aims to fool the discriminator, while the discriminator aims to correctly identify real samples. This adversarial process leads to the generation of realistic data. GANs have been successfully applied to various tasks, but they still present unique challenges and research opportunities. The generator learns to approximate the data distribution without explicitly modeling the density function, making them implicit generative models. The training process involves minimizing the cost function for both the generator and the discriminator, with the generator trying to minimize the negative log-likelihood that the discriminator assigns to the wrong labels. This process is illustrated in Figure 3, where the generator and discriminator are trained simultaneously. The theoretical foundation of GANs includes the concept of Nash equilibrium, where both the generator and the discriminator have no incentive to change their strategies. However, achieving this equilibrium is challenging, and the training process often requires careful tuning. The success of GANs lies in their ability to generate realistic data without requiring explicit density modeling, making them effective for tasks such as image generation, data synthesis, and domain adaptation. Despite their success, GANs still face challenges in convergence and stability, and ongoing research aims to improve their performance and reliability. GANs have shown great promise in various applications, including image generation, data augmentation, and machine learning tasks where labeled data is scarce.