Generative Adversarial Networks (GANs)

by: ExpertAI

Generative Adversarial Networks (GANs)

Today's lesson is about generative adversarial networks (GANs), a type of deep learning model that is used for generating new data that resembles a given training dataset.

GANs consist of two main components: a generator and a discriminator. The generator takes random noise as input and tries to generate data that resembles the training data. The discriminator, on the other hand, tries to distinguish between real training data and the data generated by the generator.

During training, the generator and discriminator are trained together in a competitive setting. The generator learns to produce more realistic data by receiving feedback from the discriminator, while the discriminator improves its ability to distinguish real data from the generated data. This iterative training process continues until the generator is able to generate data that is indistinguishable from real data according to the discriminator.

GANs have shown remarkable results in various domains, including image synthesis, text generation, and even music generation. They have been used to create realistic images, generate new artworks, and assist in data augmentation for training other models.

However, training GANs can be challenging. Finding the right balance between the generator and discriminator, as well as avoiding issues like mode collapse (where the generator only generates a limited set of outputs), can be difficult.

To address these challenges, researchers have proposed several techniques, such as Wasserstein GANs, progressive growing GANs, and conditional GANs, among others. These techniques aim to stabilize the training process, improve the quality of generated samples, and enable control over the generated outputs.

In summary, generative adversarial networks (GANs) are powerful deep learning models used for generating new data that resembles a given training dataset. GANs consist of a generator and a discriminator, trained together in a competitive setting. While training GANs can be challenging, they have achieved impressive results in various domains and continue to be an active area of research in the field of deep learning.

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About

Irfan TOOR

Irfan TOOR is an Engineer (Civil + AI), and an Innovator, passionate about the Information Technology, Digital Transformation, Cyberspace and Cybersecurity.

He obtained his first degree in Civil Engineering from UET (1993), and obtained his second degree of Engineering in Artificial Intelligence (Master 2) from OC (2023).

In his own words: "I love coding, optimizing and at times re-inventing."
— Irfan TOOR

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