Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments

Understand

One of the most significant challenges in statistical signal processing and machine learning is how to obtain a generative model that can produce samples of large-scale data distribution, such as images and speeches.

• Generative Adversarial Network (GAN) is an effective method to address this problem.
• The GANs provide an appropriate way to learn deep representations without widespread use of labeled training data.
• This approach has attracted the attention of many researchers in computer vision since it can generate a large amount of data without precise modeling of the probability density function (PDF).