The generative model is a type of machine learning model aimed at learning basic patterns or data distributions in order to create new similar data. We can conceptualize generative models as teaching computers how to imagine their own data based on what they have seen before. The importance of this model lies in its creativity, which has wide applications in various fields, from art to science to medicine.
The primary function of generative models is to understand and capture basic patterns or distributions from a certain dataset. Once these patterns are learned, the model can then generate new data that shares similar characteristics with the original dataset.
Now, to gain a deeper understanding of these generative models, let’s imagine we have a child and we’re trying to teach them how to draw animals. After showing them several pictures of different animals, the child begins to understand the general features of each animal. Over time, the child may draw an animal they haven’t seen before, combining features they’ve learned. This is similar to how a generative model works—it learns from the data it’s exposed to and then creates something new based on that knowledge.
One of the most important neural architectures specialized in generative models is GAN (Generative Adversarial Network), which has various structures to solve problems. A GAN network consists of two neural networks working together: the generator and the discriminator, which act like a teacher and a student or a policeman. The generator’s task is to generate data, for example, an image that should look like real-world data. The discriminator’s function is to examine the data generated by the generator and distinguish it from real-world data—it should look at the generated data and determine if it’s fake.
Cat and Mouse Game
According to the mentioned methodology, the game of cat and mouse begins. Since the discriminator says the data is fake, the generator tries to improve itself until it can produce more realistic data that the discriminator cannot judge as real or fake. Once this happens, the discriminator learns that it’s failing to distinguish properly, so it tries to improve itself, and next time, it judges better. Now, the ball is in the generator’s court (where the generator focuses on the patterns the discriminator relies on to detect the fakeness of what it generates, aiming to avoid them and generate models that the discriminator can’t distinguish from real data), and the game of trying to outsmart each other continues until a stage is reached where the discrimination is perfect in determining whether the incoming data from the generator is real or fake. Now, the generator wins, and we support the generator all the time, as it needs to outsmart the discriminator at its peak where victory is the best.
What is the role of generative models in the world of artificial intelligence?
Generative models play a pivotal role in tasks that require creating new content. This could be in the form of composing realistic human faces, composing music, or even generating textual content.
Prepared by: Eng.Khaled Alhushme