Generative Adversarial Networks (GANs)

GANs are very popular frameworks for generating high-squality data and are immensely used in academia and industry in many domains.

GANs are a powerful class of neural networks used for unsupervised learning. It was developed and introduced by Ian J. Goodfellow in 2014. There are two competing neural network models in a GAN, and they compete with each other to capture and replicate the differences within a dataset.

A discriminator and a generator are both present in GANs. The Generator generates fake data samples (be it an image, audio, etc.) and tries to fool the Discriminator. Discriminator tries to tell the difference between authentic and bogus samples. Because they are both neural networks, the Generator and Discriminator are constantly competing during the training process.



“Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. … This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world.”

Simply put, machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data.

While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks “smartly.” Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. (c:




Mehmet Akif Cifci

As a Ph.D. researcher, enjoying explaining complex things in simple terms. AI and ML expert.