Category: Generative Adversarial Networks (GANs)

In this tutorial, we will be implementing the Deep Convolutional Generative Adversarial Network architecture (DCGAN). We will go through the paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks first. This paper by Alec Radford, Luke Metz, and Soumith Chintala was released in 2016 and has become the baseline for many Convolutional GAN architectures […] ...

In this blog post, we train a Vanilla GAN using PyTorch to generate images from the MNIST digit dataset. ...

Generative Adversarial Networks (GANs) belong to the family of generative models. They use the techniques of deep learning and neural network models. We can use GANs to generative many types of new data including images, texts, and even tabular data. In recent years, GANs have gained much popularity in the field of deep learning. And […] ...