Summary of 125 - What are Generative Adversarial Networks (GAN)?

00:00:00 - 00:10:00

This video explains how GANs are used to generate realistic images that don't exist in the original data set. The generator network creates fake data that looks realistic, while the discriminator network tries to determine if the data is fake or not. The five steps necessary for training a GAN are (1) defining the architecture, (2) training the discriminator, (3) training the generator, (4) holding the generator values constant during the discriminator's training, and (5) training the discriminator and generator simultaneously.

• 00:00:00 This video explains generative adversarial networks (GANs). GANs are a type of artificial intelligence that are used to generate realistic images that are fake. The generator network creates fake data that looks realistic, while the discriminator network tries to determine if the data is fake or not.
• 00:05:00 Generative adversarial networks are a type of machine learning algorithm that can be used to generate realistic images that don't exist in the original data set. The five steps necessary for training a GAN are (1) defining the architecture, (2) training the discriminator, (3) training the generator, (4) holding the generator values constant during the discriminator's training, and (5) training the discriminator and generator simultaneously.
• 00:10:00 This video explains how generative adversarial networks (GANs) are used to generate high resolution images. The video also shows an example of how the network is implemented in Python.