locdacknownothing / gan_mnist

A simple project to understand basic concept of Generative Adversarial Networks (GAN)

GAN_MNIST

A simple project to understand basic concept of Generative Adversarial Networks (GAN)

GANs have become a popular deep learning model for generating new data that is similar to the training data. The model consists of two neural networks: a generator network and a discriminator network. The generator network takes random noise as input and generates fake data that is similar to the training data, while the discriminator network takes both real and fake data as input and tries to distinguish between them.

Through adversarial training, the two networks work together to improve their performance. The generator network tries to produce data that is increasingly realistic and can fool the discriminator network, while the discriminator network learns to accurately classify input data as real or fake.

As a result, GANs have shown impressive results in various applications, such as image and video synthesis, data augmentation, and more recently, in generating photorealistic images of faces and landscapes.