S01 GAN implementation (MNIST handwritten digit dataset)
S02 CGAN implementation (MNIST handwritten digit dataset)
S03 Semi-supervised GAN implementation (MNIST handwritten digit dataset)
S04 Conditional GAN implementation (MNIST handwritten digit dataset)
Generative Adversarial Networks (GANs) were introduced in 2014 by Ian J. Goodfellow and co-authors. GANs perform unsupervised learning tasks in machine learning. It consists of 2 models that automatically discover and learn the patterns in input data.
The two models are known as Generator and Discriminator.
They compete with each other to scrutinize, capture, and replicate the variations within a dataset. GANs can be used to generate new examples that plausibly could have been drawn from the original dataset.
