Generative Adversarial Networks (GANs)

In this post, we implement two GAN variants: Wasserstein GAN (WGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP), to address the training instability discussed in my previous post, GAN Training Challenges: DCGAN for Color Images. We will train the WGAN…

In this tutorial, you will learn how to train a DCGAN to generate fashion images in color. You will learn the common challenges, techniques to address these challenges, and GAN evaluation metrics through the training process. This lesson is the…

In this tutorial, we are implementing a Deep Convolutional GAN (DCGAN) with TensorFlow 2 / Keras, based on the paper, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (Radford et al., 2016). This was one of the earliest GAN…

In this tutorial, we will learn how to train our first DCGAN Model using PyTorch to generate images. This lesson is part 1 of a 3-part series on Advanced PyTorch Techniques: Training a DCGAN in PyTorch (today’s tutorial) Training an…

This post covers the intuition of Generative Adversarial Networks (GANs) at a high level, the various GAN variants, and applications for solving real-world problems. This is the first post of a GAN tutorial series: Intro to Generative Adversarial Networks (GANs)…

In this blog post, I interview Askat Kuzdeuov, a computer vision and deep learning researcher at the Institute of Smart Systems and Artificial Intelligence (ISSAI). Askat is not only a stellar researcher, but he’s an avid PyImageSearch reader as well.…

In this tutorial you will learn how to implement Generative Adversarial Networks (GANs) using Keras and TensorFlow. Generative Adversarial Networks were first introduced by Goodfellow et al. in their 2014 paper, Generative Adversarial Networks. These networks can be used to…